Systems and methods for route prediction

ABSTRACT

Predicting the future location of a user based on predicting the route that the user might take is disclosed. The routes used by the user in the past are indexed to generate a dictionary of routes which can be further augmented with contextual data. The prior routes are encoded within the dictionary such that each term representing a respective one of the prior routes comprises a collection of unique identifiers wherein each of the unique identifiers represents a segment of the respective one of the prior routes. Techniques of text prediction, term frequency for dictionary scores and other language processing techniques are used to predict the further route of the user.

RELATED APPLICATIONS

The present application claims priority to the U.S. Provisional PatentApplication Ser. No. 61/724,750 filed Nov. 9, 2012 and entitled “Systemsand methods for route prediction,” the disclosure of which applicationis hereby incorporated herein by reference.

FIELD OF THE TECHNOLOGY

At least some embodiments of the present disclosure relate to locationbased services wherein the services offered to the user can beappropriately targeted via predicting a route that a user will be likelyto traverse.

BACKGROUND

Millions of transactions occur daily through the use of payment cards,such as credit cards, debit cards, prepaid cards, etc. Correspondingrecords of the transactions are recorded in databases for settlement andfinancial record keeping (e.g., to meet the requirements of governmentregulations). Such data can be mined and analyzed for trends,statistics, and other analyses. Sometimes such data are mined forspecific advertising goals, such as to provide targeted offers toaccount holders, as described in PCT Pub. No. WO 2008/067543 A2,published on Jun. 5, 2008 and entitled “Techniques for Targeted Offers.”The data mined can include user behavior such as daily routines,spending habits and location information.

U.S. Pat. App. Pub. No. 2009/0216579, published on Aug. 27, 2009 andentitled “Tracking Online Advertising using Payment Services,” disclosesa system in which a payment service identifies the activity of a userusing a payment card as corresponding with an offer associated with anonline advertisement presented to the user.

U.S. Pat. No. 6,298,330, issued on Oct. 2, 2001 and entitled“Communicating with a Computer Based on the Offline Purchase History ofa Particular Consumer,” discloses a system in which a targetedadvertisement is delivered to a computer in response to receiving anidentifier, such as cookie, corresponding to the computer.

U.S. Pat. No. 7,035,855, issued on Apr. 25, 2006 and entitled “Processand System for Integrating Information from Disparate Databases forPurposes of Predicting Consumer Behavior,” discloses a system in whichconsumer transactional information is used for predicting consumerbehavior.

U.S. Pat. No. 6,505,168, issued on Jan. 7, 2003 and entitled “System andMethod for Gathering and Standardizing Customer Purchase Information forTarget Marketing,” discloses a system in which categories andsub-categories are used to organize purchasing information by creditcards, debit cards, checks and the like. The customer purchaseinformation is used to generate customer preference information formaking targeted offers.

U.S. Pat. No. 7,444,658, issued on Oct. 28, 2008 and entitled “Methodand System to Perform Content Targeting,” discloses a system in whichadvertisements are selected to be sent to users based on a userclassification performed using credit card purchasing data.

U.S. Pat. App. Pub. No. 2005/0055275, published on Mar. 10, 2005 andentitled “System and Method for Analyzing Marketing Efforts,” disclosesa system that evaluates the cause and effect of advertising andmarketing programs using card transaction data.

U.S. Pat. App. Pub. No. 2008/0217397, published on Sep. 11, 2008 andentitled “Real-Time Awards Determinations,” discloses a system forfacilitating transactions with real-time awards determinations for acardholder, in which the award may be provided to the cardholder as acredit on the cardholder's statement.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates a system to provide services based on transactiondata according to one embodiment.

FIG. 2 illustrates the generation of an aggregated spending profileaccording to one embodiment.

FIG. 3 shows a method to generate an aggregated spending profileaccording to one embodiment.

FIG. 4 shows a system to provide information based on transaction dataaccording to one embodiment.

FIG. 5 illustrates a transaction terminal according to one embodiment.

FIG. 6 illustrates an account identifying device according to oneembodiment.

FIG. 7 illustrates a data processing system according to one embodiment.

FIG. 8 shows the structure of account data for providing loyaltyprograms according to one embodiment.

FIG. 9 shows a system to provide real-time messages according to oneembodiment.

FIG. 10 shows a method to provide real-time messages according to oneembodiment.

FIG. 11 shows a schematic diagram wherein a route provider thatinteracts with a location provider associated with a user in accordancewith an embodiment.

FIG. 12 shows a schematic diagram of a route provider in accordance withan embodiment.

FIG. 13 shows a method of predicting a route and updating the routedictionary by a computing apparatus according to one embodiment.

FIG. 14 is a map showing various routes to frequent destinations of auser who employs a routing device wherein each intersection of tworoutes is encoded as a vertex defined in terms of a Cartesian coordinatesystem by a computing apparatus executing the route dictionary builderaccording to one embodiment.

FIG. 15 shows a map wherein each route segment between two vertices isdefined or encoded in the route dictionary in terms of the vertices bythe route dictionary builder according to one embodiment.

FIG. 16 shows a map wherein different route segments associated withvarious vertices are further assigned arbitrary identifiers, such as,letters of a language by the route dictionary builder according to oneembodiment.

FIG. 17 illustrates a route starting from the user's residence andending at the factory encoded as a word in the route dictionaryaccording to one embodiment.

FIG. 18 is a flowchart detailing a method of generating the routedictionary by the route dictionary builder according to one embodiment.

FIG. 19 is an illustration showing example entries in a route dictionarywhich encodes routes as words.

FIG. 20 is an illustration showing an example of route pruning inaccordance with an embodiment of the present disclosure.

FIG. 21 is a map showing an example route prediction from the routedictionary entries shown in FIG. 19 according to one embodiment.

FIG. 22 shows an example embodiment of a route dictionary includingroute context information.

FIG. 23 illustrates a map that shows route prediction based on contextaccording to one embodiment.

FIG. 24 shows a flowchart that details a method of providing routepredictions in accordance with embodiments described herein.

FIG. 25 shows a method to provide benefits according to one embodiment.

DETAILED DESCRIPTION Introduction

In one embodiment, transaction data, such as records of transactionsmade via credit accounts, debit accounts, prepaid accounts, bankaccounts, stored value accounts and the like, is processed to provideinformation for various services, such as reporting, benchmarking,advertising, content or offer selection, customization, personalization,prioritization, etc.

In one embodiment, an advertising network is provided based on atransaction handler to present personalized or targetedadvertisements/offers on behalf of advertisers. A computing apparatusof, or associated with, the transaction handler uses the transactiondata and/or other data, such as account data, merchant data, searchdata, social networking data, web data, etc., to develop intelligenceinformation about individual customers, or certain types or groups ofcustomers. The intelligence information can be used to select, identify,generate, adjust, prioritize, and/or personalize advertisements/offersto the customers.

In one embodiment, the computing apparatus is to generate triggerrecords for a transaction handler to identify authorization requeststhat satisfy the conditions specified in the trigger records, identifycommunication references of the users associated with the identifiedauthorization requests, and use the communication references to targetreal-time messages at the users in parallel with the transaction handlerproviding responses to the respective authorization requests. Details inone embodiment regarding the generation and delivery of messages inreal-time with the processing of transactions are provided in thesection entitled “REAL-TIME MESSAGES.”

In one embodiment, the computing apparatus correlates transactions withactivities that occurred outside the context of the transaction, such asonline advertisements presented to the customers that at least in partcause the offline transactions. The correlation data can be used todemonstrate the success of the advertisements, and/or to improveintelligence information about how individual customers and/or varioustypes or groups of customers respond to the advertisements.

In one embodiment, the computing apparatus correlates, or providesinformation to facilitate the correlation of, transactions with onlineactivities of the customers, such as searching, web browsing, socialnetworking and consuming advertisements, with other activities, such aswatching television programs, and/or with events, such as meetings,announcements, natural disasters, accidents, news announcements, etc.

In one embodiment, the correlation results are used in predictive modelsto predict transactions and/or spending patterns based on activities orevents, to predict activities or events based on transactions orspending patterns, to provide alerts or reports, etc.

In one embodiment, a single entity operating the transaction handlerperforms various operations in the services provided based on thetransaction data. For example, in the presentation of the personalizedor targeted advertisements, the single entity may perform the operationssuch as generating the intelligence information, selecting relevantintelligence information for a given audience, selecting, identifying,adjusting, prioritizing, personalizing and/or generating advertisementsbased on selected relevant intelligence information, and facilitatingthe delivery of personalized or targeted advertisements, etc.Alternatively, the entity operating the transaction handler cooperateswith one or more other entities by providing information to theseentities to allow these entities to perform at least some of theoperations for presentation of the personalized or targetedadvertisements.

In one embodiment, the personalized or targeted advertisements can beprovided as real-time messages to a user based on a predicted travelroute of the user. A computing apparatus receives a portion of a routetraversed by a user. A route dictionary encoding prior routes traversedby the user is accessed and a partial term related to the portion of theroute traversed by the user is retrieved. In an embodiment, the priorroutes are encoded such that each term representing a respective one ofthe prior routes comprises a collection of unique identifiers whereineach of the unique identifiers represents a segment of the respectiveone of the prior routes A likely route that will be traversed by theuser is predicted by the computing apparatus based on the partial term.Details in one embodiment regarding generating predictions for thelikely route of users are provided in the section entitled “ROUTEPREDICTION”.

System

FIG. 1 illustrates a system to provide services based on transactiondata according to one embodiment. In FIG. 1, the system includes atransaction terminal (105) to initiate financial transactions for a user(101), a transaction handler (103) to generate transaction data (109)from processing the financial transactions of the user (101) (and thefinancial transactions of other users), a profile generator (121) togenerate transaction profiles (127) based on the transaction data (109)to provide information/intelligence about user preferences and spendingpatterns, a point of interaction (107) to provide information and/oroffers to the user (101), a user tracker (113) to generate user data(125) to identify the user (101) using the point of interaction (107), aprofile selector (129) to select a profile (131) specific to the user(101) identified by the user data (125), and an advertisement selector(133) to select, identify, generate, adjust, prioritize and/orpersonalize advertisements for presentation to the user (101) on thepoint of interaction (107) via a media controller (115).

In one embodiment, the system further includes a correlator (117) tocorrelate user specific advertisement data (119) with transactionsresulting from the user specific advertisement data (119). Thecorrelation results (123) can be used by the profile generator (121) toimprove the transaction profiles (127).

In one embodiment, the transaction profiles (127) are generated from thetransaction data (109) in a way as illustrated in FIGS. 2 and 3. Forexample, in FIG. 2, an aggregated spending profile (341) is generatedvia the factor analysis (327) and cluster analysis (329) to summarize(335) the spending patterns/behaviors reflected in the transactionrecords (301).

In one embodiment, a data warehouse (149) as illustrated in FIG. 4 iscoupled with the transaction handler (103) to store the transaction data(109) and other data, such as account data (111), transaction profiles(127) and correlation results (123). In FIG. 4, a portal (143) iscoupled with the data warehouse (149) to provide data or informationderived from the transaction data (109), in response to a query requestfrom a third party or as an alert or notification message.

In FIG. 4, the transaction handler (103) is coupled between an issuerprocessor (145) in control of a consumer account (146) and an acquirerprocessor (147) in control of a merchant account (148). An accountidentification device (141) is configured to carry the accountinformation (142) that identifies the consumer account (146) with theissuer processor (145) and provide the account information (142) to thetransaction terminal (105) of a merchant to initiate a transactionbetween the user (101) and the merchant.

FIGS. 5 and 6 illustrate examples of transaction terminals (105) andaccount identification devices (141). FIG. 7 illustrates the structureof a data processing system that can be used to implement, with more orfewer elements, at least some of the components in the system, such asthe point of interaction (107), the transaction handler (103), theportal (143), the data warehouse, the account identification device(141), the transaction terminal (105), the user tracker (113), theprofile generator (121), the profile selector (129), the advertisementselector (133), the media controller (115), etc. Some embodiments usemore or fewer components than those illustrated in FIGS. 1 and 4-7, asfurther discussed in the section entitled “VARIATIONS.”

In one embodiment, the transaction data (109) relates to financialtransactions processed by the transaction handler (103); and the accountdata (111) relates to information about the account holders involved inthe transactions. Further data, such as merchant data that relates tothe location, business, products and/or services of the merchants thatreceive payments from account holders for their purchases, can be usedin the generation of the transaction profiles (127, 341).

In one embodiment, the financial transactions are made via an accountidentification device (141), such as financial transaction cards (e.g.,credit cards, debit cards, banking cards, etc.); the financialtransaction cards may be embodied in various devices, such as plasticcards, chips, radio frequency identification (RFID) devices, mobilephones, personal digital assistants (PDAs), etc.; and the financialtransaction cards may be represented by account identifiers (e.g.,account numbers or aliases). In one embodiment, the financialtransactions are made via directly using the account information (142),without physically presenting the account identification device (141).

Further features, modifications and details are provided in varioussections of this description.

Centralized Data Warehouse

In one embodiment, the transaction handler (103) maintains a centralizeddata warehouse (149) organized around the transaction data (109). Forexample, the centralized data warehouse (149) may include, and/orsupport the determination of, spend band distribution, transaction countand amount, merchant categories, merchant by state, cardholdersegmentation by velocity scores, and spending within merchant target,competitive set and cross-section.

In one embodiment, the centralized data warehouse (149) providescentralized management but allows decentralized execution. For example,a third party strategic marketing analyst, statistician, marketer,promoter, business leader, etc., may access the centralized datawarehouse (149) to analyze customer and shopper data, to providefollow-up analyses of customer contributions, to develop propensitymodels for increased conversion of marketing campaigns, to developsegmentation models for marketing, etc. The centralized data warehouse(149) can be used to manage advertisement campaigns and analyze responseprofitability.

In one embodiment, the centralized data warehouse (149) includesmerchant data (e.g., data about sellers), customer/business data (e.g.,data about buyers), and transaction records (301) between sellers andbuyers over time. The centralized data warehouse (149) can be used tosupport corporate sales forecasting, fraud analysis reporting,sales/customer relationship management (CRM) business intelligence,credit risk prediction and analysis, advanced authorization reporting,merchant benchmarking, business intelligence for small business,rewards, etc.

In one embodiment, the transaction data (109) is combined with externaldata, such as surveys, benchmarks, search engine statistics,demographics, competition information, emails, etc., to flag key eventsand data values, to set customer, merchant, data or event triggers, andto drive new transactions and new customer contacts.

Transaction Profile

In FIG. 1, the profile generator (121) generates transaction profiles(127) based on the transaction data (109), the account data (111),and/or other data, such as non-transactional data, wish lists, merchantprovided information, address information, information from socialnetwork websites, information from credit bureaus, information fromsearch engines, information about insurance claims, information from DNAdatabanks, and other examples discussed in U.S. patent application Ser.No. 12/614,603, filed Nov. 9, 2009 and entitled “Analyzing LocalNon-Transactional Data with Transactional Data in Predictive Models,”the disclosure of which is hereby incorporated herein by reference.

In one embodiment, the transaction profiles (127) provide intelligenceinformation on the behavior, pattern, preference, propensity, tendency,frequency, trend, and budget of the user (101) in making purchases. Inone embodiment, the transaction profiles (127) include information aboutwhat the user (101) owns, such as points, miles, or other rewardscurrency, available credit, and received offers, such as coupons loadedinto the accounts of the user (101). In one embodiment, the transactionprofiles (127) include information based on past offer/coupon redemptionpatterns. In one embodiment, the transaction profiles (127) includeinformation on shopping patterns in retail stores as well as online,including frequency of shopping, amount spent in each shopping trip,distance of merchant location (retail) from the address of the accountholder(s), etc.

In one embodiment, the transaction handler (103) provides at least partof the intelligence for the prioritization, generation, selection,customization and/or adjustment of the advertisement for delivery withina transaction process involving the transaction handler (103). Forexample, the advertisement may be presented to a customer in response tothe customer making a payment via the transaction handler (103).

Some of the transaction profiles (127) are specific to the user (101),or to an account of the user (101), or to a group of users of which theuser (101) is a member, such as a household, family, company,neighborhood, city, or group identified by certain characteristicsrelated to online activities, offline purchase activities, merchantpropensity, etc.

In one embodiment, the profile generator (121) generates and updates thetransaction profiles (127) in batch mode periodically. In otherembodiments, the profile generator (121) generates the transactionprofiles (127) in real-time, or just in time, in response to a requestreceived in the portal (143) for such profiles.

In one embodiment, the transaction profiles (127) include the values fora set of parameters. Computing the values of the parameters may involvecounting transactions that meet one or more criteria, and/or building astatistically-based model in which one or more calculated values ortransformed values are put into a statistical algorithm that weightseach value to optimize its collective predictiveness for variouspredetermined purposes.

Further details and examples about the transaction profiles (127) in oneembodiment are provided in the section entitled “AGGREGATED SPENDINGPROFILE.”

Non-Transactional Data

In one embodiment, the transaction data (109) is analyzed in connectionwith non-transactional data to generate transaction profiles (127)and/or to make predictive models.

In one embodiment, transactions are correlated with non-transactionalevents, such as news, conferences, shows, announcements, market changes,natural disasters, etc. to establish cause and effect relations topredict future transactions or spending patterns. For example,non-transactional data may include the geographic location of a newsevent, the date of an event from an events calendar, the name of aperformer for an upcoming concert, etc. The non-transactional data canbe obtained from various sources, such as newspapers, websites, blogs,social networking sites, etc.

In one embodiment, when the cause and effect relationships between thetransactions and non-transactional events are known (e.g., based onprior research results, domain knowledge, expertise), the relationshipscan be used in predictive models to predict future transactions orspending patterns, based on events that occurred recently or arehappening in real-time.

In one embodiment, the non-transactional data relates to events thathappened in a geographical area local to the user (101) that performedthe respective transactions. In one embodiment, a geographical area islocal to the user (101) when the distance from the user (101) tolocations in the geographical area is within a convenient range fordaily or regular travel, such as 20, 50 or 100 miles from an address ofthe user (101), or within the same city or zip code area of an addressof the user (101). Examples of analyses of local non-transactional datain connection with transaction data (109) in one embodiment are providedin U.S. patent application Ser. No. 12/614,603, filed Nov. 9, 2009 andentitled “Analyzing Local Non-Transactional Data with Transactional Datain Predictive Models,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the non-transactional data is not limited to localnon-transactional data. For example, national non-transactional data canalso be used.

In one embodiment, the transaction records (301) are analyzed infrequency domain to identify periodic features in spending events. Theperiodic features in the past transaction records (301) can be used topredict the probability of a time window in which a similar transactionwould occur. For example, the analysis of the transaction data (109) canbe used to predict when a next transaction having the periodic featurewould occur, with which merchant, the probability of a repeatedtransaction with a certain amount, the probability of exception, theopportunity to provide an advertisement or offer such as a coupon, etc.In one embodiment, the periodic features are detected through countingthe number of occurrences of pairs of transactions that occurred withina set of predetermined time intervals and separating the transactionpairs based on the time intervals. Some examples and techniques for theprediction of future transactions based on the detection of periodicfeatures in one embodiment are provided in U.S. patent application Ser.No. 12/773,770, filed May 4, 2010 and entitled “Frequency-BasedTransaction Prediction and Processing,” the disclosure of which ishereby incorporated herein by reference.

Techniques and details of predictive modeling in one embodiment areprovided in U.S. Pat. Nos. 6,119,103, 6,018,723, 6,658,393, 6,598,030,and 7,227,950, the disclosures of which are hereby incorporated hereinby reference.

In one embodiment, offers are based on the point-of-service to offereedistance to allow the user (101) to obtain in-person services. In oneembodiment, the offers are selected based on transaction history andshopping patterns in the transaction data (109) and/or the distancebetween the user (101) and the merchant. In one embodiment, offers areprovided in response to a request from the user (101), or in response toa detection of the location of the user (101). Examples and details ofat least one embodiment are provided in U.S. patent application Ser. No.11/767,218, filed Jun. 22, 2007, assigned Pub. No. 2008/0319843, andentitled “Supply of Requested Offer Based on Point-of Service to OffereeDistance,” U.S. patent application Ser. No. 11/755,575, filed May 30,2007, assigned Pub. No. 2008/0300973, and entitled “Supply of RequestedOffer Based on Offeree Transaction History,” U.S. patent applicationSer. No. 11/855,042, filed Sep. 13, 2007, assigned Pub. No.2009/0076896, and entitled “Merchant Supplied Offer to a Consumer withina Predetermined Distance,” U.S. patent application Ser. No. 11/855,069,filed Sep. 13, 2007, assigned Pub. No. 2009/0076925, and entitled“Offeree Requested Offer Based on Point-of Service to Offeree Distance,”and U.S. patent application Ser. No. 12/428,302, filed Apr. 22, 2009 andentitled “Receiving an Announcement Triggered by Location Data,” thedisclosures of which applications are hereby incorporated herein byreference.

Targeting Advertisement

In FIG. 1, an advertisement selector (133) prioritizes, generates,selects, adjusts, and/or customizes the available advertisement data(135) to provide user specific advertisement data (119) based at leastin part on the user specific profile (131). The advertisement selector(133) uses the user specific profile (131) as a filter and/or a set ofcriteria to generate, identify, select and/or prioritize advertisementdata for the user (101). A media controller (115) delivers the userspecific advertisement data (119) to the point of interaction (107) forpresentation to the user (101) as the targeted and/or personalizedadvertisement.

In one embodiment, the user data (125) includes the characterization ofthe context at the point of interaction (107). Thus, the use of the userspecific profile (131), selected using the user data (125), includes theconsideration of the context at the point of interaction (107) inselecting the user specific advertisement data (119).

In one embodiment, in selecting the user specific advertisement data(119), the advertisement selector (133) uses not only the user specificprofile (131), but also information regarding the context at the pointof interaction (107). For example, in one embodiment, the user data(125) includes information regarding the context at the point ofinteraction (107); and the advertisement selector (133) explicitly usesthe context information in the generation or selection of the userspecific advertisement data (119).

In one embodiment, the advertisement selector (133) may query forspecific information regarding the user (101) before providing the userspecific advertisement data (119). The queries may be communicated tothe operator of the transaction handler (103) and, in particular, to thetransaction handler (103) or the profile generator (121). For example,the queries from the advertisement selector (133) may be transmitted andreceived in accordance with an application programming interface orother query interface of the transaction handler (103), the profilegenerator (121) or the portal (143) of the transaction handler (103).

In one embodiment, the queries communicated from the advertisementselector (133) may request intelligence information regarding the user(101) at any level of specificity (e.g., segment level, individuallevel). For example, the queries may include a request for a certainfield or type of information in a cardholder's aggregate spendingprofile (341). As another example, the queries may include a request forthe spending level of the user (101) in a certain merchant category overa prior time period (e.g., six months).

In one embodiment, the advertisement selector (133) is operated by anentity that is separate from the entity that operates the transactionhandler (103). For example, the advertisement selector (133) may beoperated by a search engine, a publisher, an advertiser, an ad network,or an online merchant. The user specific profile (131) is provided tothe advertisement selector (133) to assist the customization of the userspecific advertisement data (119).

In one embodiment, advertising is targeted based on shopping patterns ina merchant category (e.g., as represented by a Merchant Category Code(MCC)) that has high correlation of spending propensity with othermerchant categories (e.g., other MCCs). For example, in the context of afirst MCC for a targeted audience, a profile identifying second MCCsthat have high correlation of spending propensity with the first MCC canbe used to select advertisements for the targeted audience.

In one embodiment, the aggregated spending profile (341) is used toprovide intelligence information about the spending patterns,preferences, and/or trends of the user (101). For example, a predictivemodel can be established based on the aggregated spending profile (341)to estimate the needs of the user (101). For example, the factor values(344) and/or the cluster ID (343) in the aggregated spending profile(341) can be used to determine the spending preferences of the user(101). For example, the channel distribution (345) in the aggregatedspending profile (341) can be used to provide a customized offertargeted for a particular channel, based on the spending patterns of theuser (101).

In one embodiment, mobile advertisements, such as offers and coupons,are generated and disseminated based on aspects of prior purchases, suchas timing, location, and nature of the purchases, etc. In oneembodiment, the size of the benefit of the offer or coupon is based onpurchase volume or spending amount of the prior purchase and/or thesubsequent purchase that may qualify for the redemption of the offer.Further details and examples of one embodiment are provided in U.S.patent application Ser. No. 11/960,162, filed Dec. 19, 2007, assignedPub. No. 2008/0201226, and entitled “Mobile Coupon Method and PortableConsumer Device for Utilizing Same,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, conditional rewards are provided to the user (101);and the transaction handler (103) monitors the transactions of the user(101) to identify redeemable rewards that have satisfied the respectiveconditions. In one embodiment, the conditional rewards are selectedbased on transaction data (109). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/862,487,filed Sep. 27, 2007 and entitled “Consumer Specific ConditionalRewards,” the disclosure of which is hereby incorporated herein byreference. The techniques to detect the satisfied conditions ofconditional rewards can also be used to detect the transactions thatsatisfy the conditions specified to locate the transactions that resultfrom online activities, such as online advertisements, searches, etc.,to correlate the transactions with the respective online activities.

Further details about targeted offer delivery in one embodiment areprovided in U.S. patent application Ser. No. 12/185,332, filed Aug. 4,2008, assigned Pub. No. 2010/0030644, and entitled “Targeted Advertisingby Payment Processor History of Cashless Acquired Merchant Transactionon Issued Consumer Account,” and in U.S. patent application Ser. No.12/849,793, filed Aug. 3, 2010 and entitled “Systems and Methods forTargeted Advertisement Delivery,” the disclosures of which are herebyincorporated herein by reference.

Profile Matching

In FIG. 1, the user tracker (113) obtains and generates contextinformation about the user (101) at the point of interaction (107),including user data (125) that characterizes and/or identifies the user(101). The profile selector (129) selects a user specific profile (131)from the set of transaction profiles (127) generated by the profilegenerator (121), based on matching the characteristics of thetransaction profiles (127) and the characteristics of the user data(125). For example, the user data (125) indicates a set ofcharacteristics of the user (101); and the profile selector (129)selects the user specific profile (131) that is for a particular user ora group of users and that best matches the set of characteristicsspecified by the user data (125).

In one embodiment, the profile selector (129) receives the transactionprofiles (127) in a batch mode. The profile selector (129) selects theuser specific profile (131) from the batch of transaction profiles (127)based on the user data (125). Alternatively, the profile generator (121)generates the transaction profiles (127) in real-time; and the profileselector (129) uses the user data (125) to query the profile generator(121) to generate the user specific profile (131) in real-time, or justin time. The profile generator (121) generates the user specific profile(131) that best matches the user data (125).

In one embodiment, the user tracker (113) identifies the user (101)based on the user activity on the transaction terminal (105) (e.g.,having visited a set of websites, currently visiting a type of webpages, search behavior, etc.).

In one embodiment, the user data (125) includes an identifier of theuser (101), such as a global unique identifier (GUID), a personalaccount number (PAN) (e.g., credit card number, debit card number, orother card account number), or other identifiers that uniquely andpersistently identify the user (101) within a set of identifiers of thesame type. Alternatively, the user data (125) may include otheridentifiers, such as an Internet Protocol (IP) address of the user(101), a name or user name of the user (101), or a browser cookie ID,which identify the user (101) in a local, temporary, transient and/oranonymous manner. Some of these identifiers of the user (101) may beprovided by publishers, advertisers, ad networks, search engines,merchants, or the user tracker (113). In one embodiment, suchidentifiers are correlated to the user (101) based on the overlapping orproximity of the time period of their usage to establish anidentification reference table.

In one embodiment, the identification reference table is used toidentify the account information (142) (e.g., account number (302))based on characteristics of the user (101) captured in the user data(125), such as browser cookie ID, IP addresses, and/or timestamps on theusage of the IP addresses. In one embodiment, the identificationreference table is maintained by the operator of the transaction handler(103). Alternatively, the identification reference table is maintainedby an entity other than the operator of the transaction handler (103).

In one embodiment, the user tracker (113) determines certaincharacteristics of the user (101) to describe a type or group of usersof which the user (101) is a member. The transaction profile of thegroup is used as the user specific profile (131). Examples of suchcharacteristics include geographical location or neighborhood, types ofonline activities, specific online activities, or merchant propensity.In one embodiment, the groups are defined based on aggregate information(e.g., by time of day, or household), or segment (e.g., by cluster,propensity, demographics, cluster IDs, and/or factor values). In oneembodiment, the groups are defined in part via one or more socialnetworks. For example, a group may be defined based on social distancesto one or more users on a social network website, interactions betweenusers on a social network website, and/or common data in social networkprofiles of the users in the social network website.

In one embodiment, the user data (125) may match different profiles at adifferent granularity or resolution (e.g., account, user, family,company, neighborhood, etc.), with different degrees of certainty. Theprofile selector (129) and/or the profile generator (121) may determineor select the user specific profile (131) with the finest granularity orresolution with acceptable certainty. Thus, the user specific profile(131) is most specific or closely related to the user (101).

In one embodiment, the advertisement selector (133) uses further data inprioritizing, selecting, generating, customizing and adjusting the userspecific advertisement data (119). For example, the advertisementselector (133) may use search data in combination with the user specificprofile (131) to provide benefits or offers to a user (101) at the pointof interaction (107). For example, the user specific profile (131) canbe used to personalize the advertisement, such as adjusting theplacement of the advertisement relative to other advertisements,adjusting the appearance of the advertisement, etc.

Browser Cookie

In one embodiment, the user data (125) uses browser cookie informationto identify the user (101). The browser cookie information is matched toaccount information (142) or the account number (302) to identify theuser specific profile (131), such as aggregated spending profile (341)to present effective, timely, and relevant marketing information to theuser (101), via the preferred communication channel (e.g., mobilecommunications, web, mail, email, POS, etc.) within a window of timethat could influence the spending behavior of the user (101). Based onthe transaction data (109), the user specific profile (131) can improveaudience targeting for online advertising. Thus, customers will getbetter advertisements and offers presented to them; and the advertiserswill achieve better return-on-investment for their advertisementcampaigns.

In one embodiment, the browser cookie that identifies the user (101) inonline activities, such as web browsing, online searching, and usingsocial networking applications, can be matched to an identifier of theuser (101) in account data (111), such as the account number (302) of afinancial payment card of the user (101) or the account information(142) of the account identification device (141) of the user (101). Inone embodiment, the identifier of the user (101) can be uniquelyidentified via matching IP address, timestamp, cookie ID and/or otheruser data (125) observed by the user tracker (113).

In one embodiment, a look up table is used to map browser cookieinformation (e.g., IP address, timestamp, cookie ID) to the account data(111) that identifies the user (101) in the transaction handler (103).The look up table may be established via correlating overlapping orcommon portions of the user data (125) observed by different entities ordifferent user trackers (113).

For example, in one embodiment, a first user tracker (113) observes thecard number of the user (101) at a particular IP address for a timeperiod identified by a timestamp (e.g., via an online payment process);a second user tracker (113) observes the user (101) having a cookie IDat the same IP address for a time period near or overlapping with thetime period observed by the first user tracker (113). Thus, the cookieID as observed by the second user tracker (113) can be linked to thecard number of the user (101) as observed by the first user tracker(113). The first user tracker (113) may be operated by the same entityoperating the transaction handler (103) or by a different entity. Oncethe correlation between the cookie ID and the card number is establishedvia a database or a look up table, the cookie ID can be subsequentlyused to identify the card number of the user (101) and the account data(111).

In one embodiment, the portal (143) is configured to observe a cardnumber of a user (101) while the user (101) uses an IP address to makean online transaction. Thus, the portal (143) can identify a consumeraccount (146) based on correlating an IP address used to identify theuser (101) and IP addresses recorded in association with the consumeraccount (146).

For example, in one embodiment, when the user (101) makes a paymentonline by submitting the account information (142) to the transactionterminal (105) (e.g., an online store), the transaction handler (103)obtains the IP address from the transaction terminal (105) via theacquirer processor (147). The transaction handler (103) stores data toindicate the use of the account information (142) at the IP address atthe time of the transaction request. When an IP address in the queryreceived in the portal (143) matches the IP address previously recordedby the transaction handler (103), the portal (143) determines that theuser (101) identified by the IP address in the request is the same user(101) associated with the account of the transaction initiated at the IPaddress. In one embodiment, a match is found when the time of the queryrequest is within a predetermined time period from the transactionrequest, such as a few minutes, one hour, a day, etc. In one embodiment,the query may also include a cookie ID representing the user (101).Thus, through matching the IP address, the cookie ID is associated withthe account information (142) in a persistent way.

In one embodiment, the portal (143) obtains the IP address of the onlinetransaction directly. For example, in one embodiment, a user (101)chooses to use a password in the account data (111) to protect theaccount information (142) for online transactions. When the accountinformation (142) is entered into the transaction terminal (105) (e.g.,an online store or an online shopping cart system), the user (101) isconnected to the portal (143) for the verification of the password(e.g., via a pop up window, or via redirecting the web browser of theuser (101)). The transaction handler (103) accepts the transactionrequest after the password is verified via the portal (143). Throughthis verification process, the portal (143) and/or the transactionhandler (103) obtain the IP address of the user (101) at the time theaccount information (142) is used.

In one embodiment, the web browser of the user (101) communicates theuser provided password to the portal (143) directly without goingthrough the transaction terminal (105) (e.g., the server of themerchant). Alternatively, the transaction terminal (105) and/or theacquirer processor (147) may relay the password communication to theportal (143) or the transaction handler (103).

In one embodiment, the portal (143) is configured to identify theconsumer account (146) based on the IP address identified in the userdata (125) through mapping the IP address to a street address. Forexample, in one embodiment, the user data (125) includes an IP addressto identify the user (101); and the portal (143) can use a service tomap the IP address to a street address. For example, an Internet serviceprovider knows the street address of the currently assigned IP address.Once the street address is identified, the portal (143) can use theaccount data (111) to identify the consumer account (146) that has acurrent address at the identified street address. Once the consumeraccount (146) is identified, the portal (143) can provide a transactionprofile (131) specific to the consumer account (146) of the user (101).

In one embodiment, the portal (143) uses a plurality of methods toidentify consumer accounts (146) based on the user data (125). Theportal (143) combines the results from the different methods todetermine the most likely consumer account (146) for the user data(125).

Details about the identification of consumer account (146) based on userdata (125) in one embodiment are provided in U.S. patent applicationSer. No. 12/849,798, filed Aug. 3, 2010 and entitled “Systems andMethods to Match Identifiers,” the disclosure of which is herebyincorporated herein by reference.

Close the Loop

In one embodiment, the correlator (117) is used to “close the loop” forthe tracking of consumer behavior across an on-line activity and an“off-line” activity that results at least in part from the on-lineactivity. In one embodiment, online activities, such as searching, webbrowsing, social networking, and/or consuming online advertisements, arecorrelated with respective transactions to generate the correlationresult (123) in FIG. 1. The respective transactions may occur offline,in “brick and mortar” retail stores, or online but in a context outsidethe online activities, such as a credit card purchase that is performedin a way not visible to a search company that facilitates the searchactivities.

In one embodiment, the correlator (117) is to identify transactionsresulting from searches or online advertisements. For example, inresponse to a query about the user (101) from the user tracker (113),the correlator (117) identifies an offline transaction performed by theuser (101) and sends the correlation result (123) about the offlinetransaction to the user tracker (113), which allows the user tracker(113) to combine the information about the offline transaction and theonline activities to provide significant marketing advantages.

For example, a marketing department could correlate an advertisingbudget to actual sales. For example, a marketer can use the correlationresult (123) to study the effect of certain prioritization strategies,customization schemes, etc. on the impact on the actual sales. Forexample, the correlation result (123) can be used to adjust orprioritize advertisement placement on a web site, a search engine, asocial networking site, an online marketplace, or the like.

In one embodiment, the profile generator (121) uses the correlationresult (123) to augment the transaction profiles (127) with dataindicating the rate of conversion from searches or advertisements topurchase transactions. In one embodiment, the correlation result (123)is used to generate predictive models to determine what a user (101) islikely to purchase when the user (101) is searching using certainkeywords or when the user (101) is presented with an advertisement oroffer. In one embodiment, the portal (143) is configured to report thecorrelation result (123) to a partner, such as a search engine, apublisher, or a merchant, to allow the partner to use the correlationresult (123) to measure the effectiveness of advertisements and/orsearch result customization, to arrange rewards, etc.

Illustratively, a search engine entity may display a search page withparticular advertisements for flat panel televisions produced bycompanies A, B, and C. The search engine entity may then compare theparticular advertisements presented to a particular consumer withtransaction data of that consumer and may determine that the consumerpurchased a flat panel television produced by Company B. The searchengine entity may then use this information and other informationderived from the behavior of other consumers to determine theeffectiveness of the advertisements provided by companies A, B, and C.The search engine entity can determine if the placement, the appearance,or other characteristic of the advertisement results in actual increasedsales. Adjustments to advertisements (e.g., placement, appearance, etc.)may be made to facilitate maximum sales.

In one embodiment, the correlator (117) matches the online activitiesand the transactions based on matching the user data (125) provided bythe user tracker (113) and the records of the transactions, such astransaction data (109) or transaction records (301). In anotherembodiment, the correlator (117) matches the online activities and thetransactions based on the redemption of offers/benefits provided in theuser specific advertisement data (119).

In one embodiment, the portal (143) is configured to receive a set ofconditions and an identification of the user (101), determine whetherthere is any transaction of the user (101) that satisfies the set ofconditions, and if so, provide indications of the transactions thatsatisfy the conditions and/or certain details about the transactions,which allows the requester to correlate the transactions with certainuser activities, such as searching, web browsing, consumingadvertisements, etc.

In one embodiment, the requester may not know the account number (302)of the user (101); and the portal (143) is to map the identifierprovided in the request to the account number (302) of the user (101) toprovide the requested information. Examples of the identifier beingprovided in the request to identify the user (101) include anidentification of an iFrame of a web page visited by the user (101), abrowser cookie ID, an IP address and the day and time corresponding tothe use of the IP address, etc.

The information provided by the portal (143) can be used in pre-purchasemarketing activities, such as customizing content or offers,prioritizing content or offers, selecting content or offers, etc., basedon the spending pattern of the user (101). The content that iscustomized, prioritized, selected, or recommended may be the searchresults, blog entries, items for sale, etc.

The information provided by the portal (143) can be used inpost-purchase activities. For example, the information can be used tocorrelate an offline purchase with online activities. For example, theinformation can be used to determine purchases made in response to mediaevents, such as television programs, advertisements, news announcements,etc.

Details about profile delivery, online activity to offline purchasetracking, techniques to identify the user specific profile (131) basedon user data (125) (such as IP addresses), and targeted delivery ofadvertisement/offer/benefit in some embodiments are provided in U.S.patent application Ser. No. 12/849,789, filed Aug. 3, 2010 and entitled“Systems and Methods for Closing the Loop between Online Activities andOffline Purchases,” U.S. patent application Ser. No. 12/851,138, filedAug. 5, 2010 and entitled “Systems and Methods for Propensity Analysisand Validation,” and U.S. patent application Ser. No. 12/854,022, filedAug. 10, 2010 and entitled “System and Methods for Targeting Offers,”the disclosures of which applications are incorporated herein byreference.

Matching Advertisement & Transaction

In one embodiment, the correlator (117) is configured to receiveinformation about the user specific advertisement data (119), monitorthe transaction data (109), identify transactions that can be consideredresults of the advertisement corresponding to the user specificadvertisement data (119), and generate the correlation result (123), asillustrated in FIG. 1.

When the advertisement and the corresponding transaction both occur inan online checkout process, a website used for the online checkoutprocess can be used to correlate the transaction and the advertisement.However, the advertisement and the transaction may occur in separateprocesses and/or under control of different entities (e.g., when thepurchase is made offline at a retail store, while the advertisement ispresented outside the retail store). In one embodiment, the correlator(117) uses a set of correlation criteria to identify the transactionsthat can be considered as the results of the advertisements.

In one embodiment, the correlator (117) identifies the transactionslinked or correlated to the user specific advertisement data (119) basedon various criteria. For example, the user specific advertisement data(119) may include a coupon offering a benefit contingent upon a purchasemade according to the user specific advertisement data (119). The use ofthe coupon identifies the user specific advertisement data (119), andthus allows the correlator (117) to correlate the transaction with theuser specific advertisement data (119).

In one embodiment, the user specific advertisement data (119) isassociated with the identity or characteristics of the user (101), suchas global unique identifier (GUID), personal account number (PAN),alias, IP address, name or user name, geographical location orneighborhood, household, user group, and/or user data (125). Thecorrelator (117) can link or match the transactions with theadvertisements based on the identity or characteristics of the user(101) associated with the user specific advertisement data (119). Forexample, the portal (143) may receive a query identifying the user data(125) that tracks the user (101) and/or characteristics of the userspecific advertisement data (119); and the correlator (117) identifiesone or more transactions matching the user data (125) and/or thecharacteristics of the user specific advertisement data (119) togenerate the correlation result (123).

In one embodiment, the correlator (117) identifies the characteristicsof the transactions and uses the characteristics to search foradvertisements that match the transactions. Such characteristics mayinclude GUID, PAN, IP address, card number, browser cookie information,coupon, alias, etc.

In FIG. 1, the profile generator (121) uses the correlation result (123)to enhance the transaction profiles (127) generated from the profilegenerator (121). The correlation result (123) provides details on thepurchases and/or indicates the effectiveness of the user specificadvertisement data (119).

In one embodiment, the correlation result (123) is used to demonstrateto the advertisers the effectiveness of the advertisements, to processincentive or rewards associated with the advertisements, to obtain atleast a portion of advertisement revenue based on the effectiveness ofthe advertisements, to improve the selection of advertisements, etc.

Coupon Matching

In one embodiment, the correlator (117) identifies a transaction that isa result of an advertisement (e.g., 119) when an offer or benefitprovided in the advertisement is redeemed via the transaction handler(103) in connection with a purchase identified in the advertisement.

For example, in one embodiment, when the offer is extended to the user(101), information about the offer can be stored in association with theaccount of the user (101) (e.g., as part of the account data (111)). Theuser (101) may visit the portal (143) of the transaction handler (103)to view the stored offer.

The offer stored in the account of the user (101) may be redeemed viathe transaction handler (103) in various ways. For example, in oneembodiment, the correlator (117) may download the offer to thetransaction terminal (105) via the transaction handler (103) when thecharacteristics of the transaction at the transaction terminal (105)match the characteristics of the offer.

After the offer is downloaded to the transaction terminal (105), thetransaction terminal (105) automatically applies the offer when thecondition of the offer is satisfied in one embodiment. Alternatively,the transaction terminal (105) allows the user (101) to selectivelyapply the offers downloaded by the correlator (117) or the transactionhandler (103). In one embodiment, the correlator (117) sends remindersto the user (101) at a separate point of interaction (107) (e.g., amobile phone) to remind the user (101) to redeem the offer. In oneembodiment, the transaction handler (103) applies the offer (e.g., viastatement credit), without having to download the offer (e.g., coupon)to the transaction terminal (105). Examples and details of redeemingoffers via statement credit are provided in U.S. patent application Ser.No. 12/566,350, filed Sep. 24, 2009 and entitled “Real-Time StatementCredits and Notifications,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the offer is captured as an image and stored inassociation with the account of the user (101). Alternatively, the offeris captured in a text format (e.g., a code and a set of criteria),without replicating the original image of the coupon.

In one embodiment, when the coupon is redeemed, the advertisementpresenting the coupon is correlated with a transaction in which thecoupon is redeemed, and/or is determined to have resulted in atransaction. In one embodiment, the correlator (117) identifiesadvertisements that have resulted in purchases, without having toidentify the specific transactions that correspond to theadvertisements.

Details about offer redemption via the transaction handler (103) in oneembodiment are provided in U.S. patent application Ser. No. 12/849,801,filed Aug. 3, 2010 and entitled “Systems and Methods for Multi-ChannelOffer Redemption,” the disclosure of which is hereby incorporated hereinby reference.

Loyalty Program

In one embodiment, the transaction handler (103) uses the account data(111) to store information for third party loyalty programs. Thetransaction handler (103) processes payment transactions made viafinancial transaction cards, such as credit cards, debit cards, bankingcards, etc.; and the financial transaction cards can be used as loyaltycards for the respective third party loyalty programs. Since the thirdparty loyalty programs are hosted on the transaction handler (103), theconsumers do not have to carry multiple, separate loyalty cards (e.g.,one for each merchant that offers a loyalty program); and the merchantsdo not have to spend a large setup and investment fee to establish theloyalty program. The loyalty programs hosted on the transaction handler(103) can provide flexible awards for consumers, retailers,manufacturers, issuers, and other types of business entities involved inthe loyalty programs. The integration of the loyalty programs into theaccounts of the customers on the transaction handler (103) allows newofferings, such as merchant cross-offerings or bundling of loyaltyofferings.

In one embodiment, an entity operating the transaction handler (103)hosts loyalty programs for third parties using the account data (111) ofthe users (e.g., 101). A third party, such as a merchant, a retailer, amanufacturer, an issuer or other entity that is interested in promotingcertain activities and/or behaviors, may offer loyalty rewards onexisting accounts of consumers. The incentives delivered by the loyaltyprograms can drive behavior changes without the hassle of loyalty cardcreation. In one embodiment, the loyalty programs hosted via theaccounts of the users (e.g., 101) of the transaction handler (103) allowthe consumers to carry fewer cards and may provide more data to themerchants than traditional loyalty programs.

The loyalty programs integrated with the accounts of the users (e.g.,101) of the transaction handler (103) can provide tools to enable nimbleprograms that are better aligned for driving changes in consumerbehaviors across transaction channels (e.g., online, offline, via mobiledevices). The loyalty programs can be ongoing programs that accumulatebenefits for the customers (e.g., points, miles, cash back), and/orprograms that provide one time benefits or limited time benefits (e.g.,rewards, discounts, incentives).

FIG. 8 shows the structure of account data (111) for providing loyaltyprograms according to one embodiment. In FIG. 8, data related to a thirdparty loyalty program may include an identifier of the loyalty benefitofferor (183) that is linked to a set of loyalty program rules (185) andloyalty record (187) for the loyalty program activities of the accountidentifier (181). In one embodiment, at least part of the data relatedto the third party loyalty program is stored under the accountidentifier (181) of the user (101), such as the loyalty record (187).

FIG. 8 illustrates the data related to one third party loyalty programof a loyalty benefit offeror (183). In one embodiment, the accountidentifier (181) may be linked to multiple loyalty benefit offerors(e.g., 183), corresponding to different third party loyalty programs.

In one embodiment, a third party loyalty program of the loyalty benefitofferor (183) provides the user (101), identified by the accountidentifier (181), with benefits, such as discounts, rewards, incentives,cash back, gifts, coupons, and/or privileges.

In one embodiment, the association between the account identifier (181)and the loyalty benefit offeror (183) in the account data (111)indicates that the user (101) having the account identifier (181) is amember of the loyalty program. Thus, the user (101) may use the accountidentifier (181) to access privileges afforded to the members of theloyalty programs, such as rights to access a member only area, facility,store, product or service, discounts extended only to members, oropportunities to participate in certain events, buy certain items, orreceive certain services reserved for members.

In one embodiment, it is not necessary to make a purchase to use theprivileges. The user (101) may enjoy the privileges based on the statusof being a member of the loyalty program. The user (101) may use theaccount identifier (181) to show the status of being a member of theloyalty program.

For example, the user (101) may provide the account identifier (181)(e.g., the account number of a credit card) to the transaction terminal(105) to initiate an authorization process for a special transactionwhich is designed to check the member status of the user (101), as ifthe account identifier (181) were used to initiate an authorizationprocess for a payment transaction. The special transaction is designedto verify the member status of the user (101) via checking whether theaccount data (111) is associated with the loyalty benefit offeror (183).If the account identifier (181) is associated with the correspondingloyalty benefit offeror (183), the transaction handler (103) provides anapproval indication in the authorization process to indicate that theuser (101) is a member of the loyalty program. The approval indicationcan be used as a form of identification to allow the user (101) toaccess member privileges, such as access to services, products,opportunities, facilities, discounts, permissions, which are reservedfor members.

In one embodiment, when the account identifier (181) is used to identifythe user (101) as a member to access member privileges, the transactionhandler (103) stores information about the access of the correspondingmember privilege in loyalty record (187). The profile generator (121)may use the information accumulated in the loyalty record (187) toenhance transaction profiles (127) and provide the user (101) withpersonalized/targeted advertisements, with or without further offers ofbenefit (e.g., discounts, incentives, rebates, cash back, rewards,etc.).

In one embodiment, the association of the account identifier (181) andthe loyalty benefit offeror (183) also allows the loyalty benefitofferor (183) to access at least a portion of the account data (111)relevant to the loyalty program, such as the loyalty record (187) andcertain information about the user (101), such as name, address, andother demographic data.

In one embodiment, the loyalty program allows the user (101) toaccumulate benefits according to loyalty program rules (185), such asreward points, cash back, levels of discounts, etc. For example, theuser (101) may accumulate reward points for transactions that satisfythe loyalty program rules (185); and the user (101) may use the rewardpoints to redeem cash, gift, discounts, etc. In one embodiment, theloyalty record (187) stores the accumulated benefits; and thetransaction handler (103) updates the loyalty record (187) associatedwith the loyalty benefit offeror (183) and the account identifier (181),when events that satisfy the loyalty program rules occur.

In one embodiment, the accumulated benefits as indicated in the loyaltyrecord (187) can be redeemed when the account identifier (181) is usedto perform a payment transaction, when the payment transaction satisfiesthe loyalty program rules. For example, the user (101) may redeem anumber of points to offset or reduce an amount of the purchase price.

In one embodiment, when the user (101) uses the account identifier (181)to make purchases as a member, the merchant may further provideinformation about the purchases; and the transaction handler (103) canstore the information about the purchases as part of the loyalty record(187). The information about the purchases may identify specific itemsor services purchased by the member. For example, the merchant mayprovide the transaction handler (103) with purchase details atstock-keeping unit (SKU) level, which are then stored as part of theloyalty record (187). The loyalty benefit offeror (183) may use thepurchase details to study the purchase behavior of the user (101); andthe profile generator (121) may use the SKU level purchase details toenhance the transaction profiles (127).

In one embodiment, the SKU level purchase details are requested from themerchants or retailers via authorization responses, when the account(146) of the user (101) is enrolled in a loyalty program that allows thetransaction handler (103) (and/or the issuer processor (145)) to collectthe purchase details.

In one embodiment, the profile generator (121) may generate transactionprofiles (127) based on the loyalty record (187) and provide thetransaction profiles (127) to the loyalty benefit offeror (183) (orother entities when permitted).

In one embodiment, the loyalty benefit offeror (183) may use thetransaction profiles (e.g., 127 or 131) to select candidates formembership offering. For example, the loyalty program rules (185) mayinclude one or more criteria that can be used to identify whichcustomers are eligible for the loyalty program. The transaction handler(103) may be configured to automatically provide the qualified customerswith the offer of membership in the loyalty program when thecorresponding customers are performing transactions via the transactionhandler (103) and/or via points of interaction (107) accessible to theentity operating the transaction handler (103), such as ATMs, mobilephones, receipts, statements, websites, etc. The user (101) may acceptthe membership offer via responding to the advertisement. For example,the user (101) may load the membership into the account in the same wayas loading a coupon into the account of the user (101).

In one embodiment, the membership offer is provided as a coupon or isassociated with another offer of benefits, such as a discount, reward,etc. When the coupon or benefit is redeemed via the transaction handler(103), the account data (111) is updated to enroll the user (101) intothe corresponding loyalty program.

In one embodiment, a merchant may enroll a user (101) into a loyaltyprogram when the user (101) is making a purchase at the transactionterminal (105) of the merchant.

For example, when the user (101) is making a transaction at an ATM,performing a self-assisted check out on a POS terminal, or making apurchase transaction on a mobile phone or a computer, the user (101) maybe prompted to join a loyalty program, while the transaction is beingauthorized by the transaction handler (103). If the user (101) acceptsthe membership offer, the account data (111) is updated to have theaccount identifier (181) associated with the loyalty benefit offeror(183).

In one embodiment, the user (101) may be automatically enrolled in theloyalty program, when the profile of the user (101) satisfies a set ofconditions specified in the loyalty program rules (185). The user (101)may opt out of the loyalty program.

In one embodiment, the loyalty benefit offeror (183) may personalizeand/or target loyalty benefits based on the transaction profile (131)specific to or linked to the user (101). For example, the loyaltyprogram rules (185) may use the user specific profile (131) to selectgifts, rewards, or incentives for the user (101) (e.g., to redeembenefits, such as reward points, accumulated in the loyalty record(187)). The user specific profile (131) may be enhanced using theloyalty record (187), or generated based on the loyalty record (187).For example, the profile generator (121) may use a subset of transactiondata (109) associated with the loyalty record (187) to generate the userspecific profile (131), or provide more weight to the subset of thetransaction data (109) associated with the loyalty record (187) whilealso using other portions of the transaction data (109) in deriving theuser specific profile (131).

In one embodiment, the loyalty program may involve different entities.For example, a first merchant may offer rewards as discounts, or giftsfrom a second merchant that has a business relationship with the firstmerchant. For example, an entity may allow a user (101) to accumulateloyalty benefits (e.g., reward points) via purchase transactions at agroup of different merchants. For example, a group of merchants mayjointly offer a loyalty program, in which loyalty benefits (e.g., rewardpoints) can be accumulated from purchases at any of the merchants in thegroup and redeemable in purchases at any of the merchants.

In one embodiment, the information identifying the user (101) as amember of a loyalty program is stored on a server connected to thetransaction handler (103). Alternatively or in combination, theinformation identifying the user (101) as a member of a loyalty programcan also be stored in the financial transaction card (e.g., in the chip,or in the magnetic strip).

In one embodiment, loyalty program offerors (e.g., merchants,manufactures, issuers, retailers, clubs, organizations, etc.) cancompete with each other in making loyalty program related offers. Forexample, loyalty program offerors may place bids on loyalty programrelated offers; and the advertisement selector (133) (e.g., under thecontrol of the entity operating the transaction handler (103), or adifferent entity) may prioritize the offers based on the bids. When theoffers are accepted or redeemed by the user (101), the loyalty programofferors pay fees according to the corresponding bids. In oneembodiment, the loyalty program offerors may place an auto bid ormaximum bid, which specifies the upper limit of a bid; and the actualbid is determined to be the lowest possible bid that is larger than thebids of the competitors, without exceeding the upper limit.

In one embodiment, the offers are provided to the user (101) in responseto the user (101) being identified by the user data (125). If the userspecific profile (131) satisfies the conditions specified in the loyaltyprogram rules (185), the offer from the loyalty benefit offeror (183)can be presented to the user (101). When there are multiple offers fromdifferent offerors, the offers can be prioritized according to the bids.

In one embodiment, the offerors can place bids based on thecharacteristics that can be used as the user data (125) to select theuser specific profile (131). In another embodiment, the bids can beplaced on a set of transaction profiles (127).

In one embodiment, the loyalty program based offers are provided to theuser (101) just in time when the user (101) can accept and redeem theoffers. For example, when the user (101) is making a payment for apurchase from a merchant, an offer to enroll in a loyalty programoffered by the merchant or related offerors can be presented to the user(101). If the user (101) accepts the offer, the user (101) is entitledto receive member discounts for the purchase.

For example, when the user (101) is making a payment for a purchase froma merchant, a reward offer can be provided to the user (101) based onloyalty program rules (185) and the loyalty record (187) associated withthe account identifier (181) of the user (101) (e.g., the reward pointsaccumulated in a loyalty program). Thus, the user effort for redeemingthe reward points can be reduced; and the user experience can beimproved.

In one embodiment, a method to provide loyalty programs includes the useof a computing apparatus of a transaction handler (103). The computingapparatus processes (301) a plurality of payment card transactions.After the computing apparatus receives (303) a request to tracktransactions for a loyalty program, such as the loyalty program rules(185), the computing apparatus stores and updates (305) loyalty programinformation in response to transactions occurring in the loyaltyprogram. The computing apparatus provides (307) to a customer (e.g.,101) an offer of a benefit when the customer satisfies a conditiondefined in the loyalty program, such as the loyalty program rules (185).

Examples of loyalty programs through collaboration between collaborativeconstituents in a payment processing system, including the transactionhandler (103) in one embodiment are provided in U.S. patent applicationSer. No. 11/767,202, filed Jun. 22, 2007, assigned Pub. No.2008/0059302, and entitled “Loyalty Program Service,” U.S. patentapplication Ser. No. 11/848,112, filed Aug. 30, 2007, assigned Pub. No.2008/0059306, and entitled “Loyalty Program Incentive Determination,”and U.S. patent application Ser. No. 11/848,179, filed Aug. 30, 2007,assigned Pub. No. 2008/0059307, and entitled “Loyalty Program ParameterCollaboration,” the disclosures of which applications are herebyincorporated herein by reference.

Examples of processing the redemption of accumulated loyalty benefitsvia the transaction handler (103) in one embodiment are provided in U.S.patent application Ser. No. 11/835,100, filed Aug. 7, 2007, assignedPub. No. 2008/0059303, and entitled “Transaction Evaluation forProviding Rewards,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the incentive, reward, or benefit provided in theloyalty program is based on the presence of correlated relatedtransactions. For example, in one embodiment, an incentive is providedif a financial payment card is used in a reservation system to make areservation and the financial payment card is subsequently used to payfor the reserved good or service. Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/945,907,filed Nov. 27, 2007, assigned Pub. No. 2008/0071587, and entitled“Incentive Wireless Communication Reservation,” the disclosure of whichis hereby incorporated herein by reference.

In one embodiment, the transaction handler (103) provides centralizedloyalty program management, reporting and membership services. In oneembodiment, membership data is downloaded from the transaction handler(103) to acceptance point devices, such as the transaction terminal(105). In one embodiment, loyalty transactions are reported from theacceptance point devices to the transaction handler (103); and the dataindicating the loyalty points, rewards, benefits, etc. are stored on theaccount identification device (141). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 10/401,504,filed Mar. 27, 2003, assigned Pub. No. 2004/0054581, and entitled“Network Centric Loyalty System,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the portal (143) of the transaction handler (103) isused to manage reward or loyalty programs for entities such as issuers,merchants, etc. The cardholders, such as the user (101), are rewardedwith offers/benefits from merchants. The portal (143) and/or thetransaction handler (103) track the transaction records for themerchants for the reward or loyalty programs. Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 11/688,423, filed Mar. 20, 2007, assigned Pub. No. 2008/0195473, andentitled “Reward Program Manager,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, a loyalty program includes multiple entitiesproviding access to detailed transaction data, which allows theflexibility for the customization of the loyalty program. For example,issuers or merchants may sponsor the loyalty program to provide rewards;and the portal (143) and/or the transaction handler (103) stores theloyalty currency in the data warehouse (149). Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 12/177,530, filed Jul. 22, 2008, assigned Pub. No. 2009/0030793, andentitled “Multi-Vender Multi-Loyalty Currency Program,” the disclosureof which is hereby incorporated herein by reference.

In one embodiment, an incentive program is created on the portal (143)of the transaction handler (103). The portal (143) collects offers froma plurality of merchants and stores the offers in the data warehouse(149). The offers may have associated criteria for their distributions.The portal (143) and/or the transaction handler (103) may recommendoffers based on the transaction data (109). In one embodiment, thetransaction handler (103) automatically applies the benefits of theoffers during the processing of the transactions when the transactionssatisfy the conditions associated with the offers. In one embodiment,the transaction handler (103) communicates with transaction terminals(105) to set up, customize, and/or update offers based on market focus,product categories, service categories, targeted consumer demographics,etc. Further details and examples of one embodiment are provided in U.S.patent application Ser. No. 12/413,097, filed Mar. 27, 2009, assignedPub. No. 2010-0049620, and entitled “Merchant Device Support of anIntegrated Offer Network,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the transaction handler (103) is configured toprovide offers from merchants to the user (101) via the payment system,making accessing and redeeming the offers convenient for the user (101).The offers may be triggered by and/or tailored to a previoustransaction, and may be valid only for a limited period of time startingfrom the date of the previous transaction. If the transaction handler(103) determines that a subsequent transaction processed by thetransaction handler (103) meets the conditions for the redemption of anoffer, the transaction handler (103) may credit the consumer account(146) for the redemption of the offer and/or provide a notificationmessage to the user (101). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 12/566,350,filed Sep. 24, 2009 and entitled “Real-Time Statement Credits andNotifications,” the disclosure of which is hereby incorporated herein byreference.

Details on loyalty programs in one embodiment are provided in U.S.patent application Ser. No. 12/896,632, filed Oct. 1, 2010 and entitled“Systems and Methods to Provide Loyalty Programs,” the disclosure ofwhich is hereby incorporated herein by reference.

SKU

In one embodiment, merchants generate stock-keeping unit (SKU) or otherspecific information that identifies the particular goods and servicespurchased by the user (101) or customer. The SKU information may beprovided to the operator of the transaction handler (103) that processedthe purchases. The operator of the transaction handler (103) may storethe SKU information as part of transaction data (109), and reflect theSKU information for a particular transaction in a transaction profile(127 or 131) associated with the person involved in the transaction.

When a user (101) shops at a traditional retail store or browses awebsite of an online merchant, an SKU-level profile associatedspecifically with the user (101) may be provided to select anadvertisement appropriately targeted to the user (101) (e.g., via mobilephones, POS terminals, web browsers, etc.). The SKU-level profile forthe user (101) may include an identification of the goods and serviceshistorically purchased by the user (101). In addition, the SKU-levelprofile for the user (101) may identify goods and services that the user(101) may purchase in the future. The identification may be based onhistorical purchases reflected in SKU-level profiles of otherindividuals or groups that are determined to be similar to the user(101). Accordingly, the return on investment for advertisers andmerchants can be greatly improved.

In one embodiment, the user specific profile (131) is an aggregatedspending profile (341) that is generated using the SKU-levelinformation. For example, in one embodiment, the factor values (344)correspond to factor definitions (331) that are generated based onaggregating spending in different categories of products and/orservices. A typical merchant offers products and/or services in manydifferent categories.

In one embodiment, the user (101) may enter into transactions withvarious online and “brick and mortar” merchants. The transactions mayinvolve the purchase of various items of goods and services. The goodsand services may be identified by SKU numbers or other information thatspecifically identifies the goods and services purchased by the user(101).

In one embodiment, the merchant may provide the SKU informationregarding the goods and services purchased by the user (101) (e.g.,purchase details at SKU level) to the operator of the transactionhandler (103). In one embodiment, the SKU information may be provided tothe operator of the transaction handler (103) in connection with aloyalty program, as described in more detail below. The SKU informationmay be stored as part of the transaction data (109) and associated withthe user (101). In one embodiment, the SKU information for itemspurchased in transactions facilitated by the operator of the transactionhandler (103) may be stored as transaction data (109) and associatedwith its associated purchaser. In one embodiment, the SKU level purchasedetails are requested from the merchants or retailers via authorizationresponses, when the account (146) of the user (101) is enrolled in aprogram that allows the transaction handler (103) (and/or the issuerprocessor (145)) to collect the purchase details.

In one embodiment, based on the SKU information and perhaps othertransaction data, the profile generator (121) may create an SKU-leveltransaction profile for the user (101). In one embodiment, based on theSKU information associated with the transactions for each personentering into transactions with the operator of the transaction handler(103), the profile generator (121) may create an SKU-level transactionprofile for each person.

In one embodiment, the SKU information associated with a group ofpurchasers may be aggregated to create an SKU-level transaction profilethat is descriptive of the group. The group may be defined based on oneor a variety of considerations. For example, the group may be defined bycommon demographic features of its members. As another example, thegroup may be defined by common purchasing patters of its members.

In one embodiment, the user (101) may later consider the purchase ofadditional goods and services. The user (101) may shop at a traditionalretailer or an online retailer. With respect to an online retailer, forexample, the user (101) may browse the website of an online retailer,publisher, or merchant. The user (101) may be associated with a browsercookie to, for example, identify the user (101) and track the browsingbehavior of the user (101).

In one embodiment, the retailer may provide the browser cookieassociated with the user (101) to the operator of the transactionhandler (103). Based on the browser cookie, the operator of thetransaction handler (103) may associate the browser cookie with apersonal account number of the user (101). The association may beperformed by the operator of the transaction handler (103) or anotherentity in a variety of manners such as, for example, using a look uptable.

Based on the personal account number, the profile selector (129) mayselect a user specific profile (131) that constitutes the SKU-levelprofile associated specifically with the user (101). The SKU-levelprofile may reflect the individual, prior purchases of the user (101)specifically, and/or the types of goods and services that the user (101)has purchased.

The SKU-level profile for the user (101) may also includeidentifications of goods and services the user (101) may purchase in thefuture. In one embodiment, the identifications may be used for theselection of advertisements for goods and services that may be ofinterest to the user (101). In one embodiment, the identifications forthe user (101) may be based on the SKU-level information associated withhistorical purchases of the user (101). In one embodiment, theidentifications for the user (101) may be additionally or alternativelybased on transaction profiles associated with others. Therecommendations may be determined by predictive association and otheranalytical techniques.

For example, the identifications for the user (101) may be based on thetransaction profile of another person. The profile selector (129) mayapply predetermined criteria to identify another person who, to apredetermined degree, is deemed sufficiently similar to the user (101).The identification of the other person may be based on a variety offactors including, for example, demographic similarity and/or purchasingpattern similarity between the user (101) and the other person. As oneexample, the common purchase of identical items or related items by theuser (101) and the other person may result in an association between theuser (101) and the other person, and a resulting determination that theuser (101) and the other person are similar. Once the other person isidentified, the transaction profile constituting the SKU-level profilefor the other person may be analyzed. Through predictive association andother modeling and analytical techniques, the historical purchasesreflected in the SKU-level profile for the other person may be employedto predict the future purchases of the user (101).

As another example, the identifications of the user (101) may be basedon the transaction profiles of a group of persons. The profile selector(129) may apply predetermined criteria to identify a multitude ofpersons who, to a predetermined degree, are deemed sufficiently similarto the user (101). The identification of the other persons may be basedon a variety of factors including, for example, demographic similarityand/or purchasing pattern similarity between the user (101) and theother persons. Once the group constituting the other persons isidentified, the transaction profile constituting the SKU-level profilefor the group may be analyzed. Through predictive association and othermodeling and analytical techniques, the historical purchases reflectedin the SKU-level profile for the group may be employed to predict thefuture purchases of the user (101).

The SKU-level profile of the user (101) may be provided to select anadvertisement that is appropriately targeted. Because the SKU-levelprofile of the user (101) may include identifications of the goods andservices that the user (101) may be likely to buy, advertisementscorresponding to the identified goods and services may be presented tothe user (101). In this way, targeted advertising for the user (101) maybe optimized. Further, advertisers and publishers of advertisements mayimprove their return on investment, and may improve their ability tocross-sell goods and services.

In one embodiment, SKU-level profiles of others who are identified to besimilar to the user (101) may be used to identify a user (101) who mayexhibit a high propensity to purchase goods and services. For example,if the SKU-level profiles of others reflect a quantity or frequency ofpurchase that is determined to satisfy a threshold, then the user (101)may also be classified or predicted to exhibit a high propensity topurchase. Accordingly, the type and frequency of advertisements thataccount for such propensity may be appropriately tailored for the user(101).

In one embodiment, the SKU-level profile of the user (101) may reflecttransactions with a particular merchant or merchants. The SKU-levelprofile of the user (101) may be provided to a business that isconsidered a peer with or similar to the particular merchant ormerchants. For example, a merchant may be considered a peer of thebusiness because the merchant offers goods and services that are similarto or related to those of the business. The SKU-level profile reflectingtransactions with peer merchants may be used by the business to betterpredict the purchasing behavior of the user (101) and to optimize thepresentation of targeted advertisements to the user (101).

Details on SKU-level profile in one embodiment are provided in U.S.patent application Ser. No. 12/899,144, filed Oct. 6, 2010 and entitled“Systems and Methods for Advertising Services Based on an SKU-LevelProfile,” the disclosure of which is hereby incorporated herein byreference.

Real-Time Messages

In one embodiment, the transaction handler (103) is configured tocooperate with the media controller (115) to facilitate real-timeinteraction with the user (101) when the payment of the user (101) isbeing processed by the transaction handler (103). The real-timeinteraction provides the opportunity to impact the user experienceduring the purchase (e.g., at the time of card swipe), throughdelivering messages in real-time to a point of interaction (107), suchas a mobile phone, a personal digital assistant, a portable computer,etc. The real-time message can be delivered via short message service(SMS), email, instant messaging, or other communications protocols.

In one embodiment, the real-time message is provided without requiringmodifications to existing systems used by the merchants and/or issuers.

FIG. 9 shows a system to provide real-time messages according to oneembodiment. In FIG. 9, the system includes a transaction handler (103),a message broker (201) and a media controller (115).

In one embodiment, the transaction handler (103) (or a separatecomputing system coupled with the transaction handler (103)) isconfigured to detect the occurrence of certain transactions of interestduring the processing of the authorization requests (e.g., 202) receivedfrom the transaction terminal (105) (via an acquirer processor (147)associated with the transaction terminal (105) of a merchant and/or themerchant account (148)).

In one embodiment, the message broker (201) is configured to identify arelevant message for the user (101) associated with the correspondingauthorization request (202); and the media controller (115) is toprovide the message to the user (101) at the point of interaction (107)via a communication channel separate from the channel used by thetransaction handler (103) to respond (206) to the correspondingauthorization request (202) submitted from the transaction terminal(105).

In one embodiment, the media controller (115) is to provide the message(204) to the point of interaction (107) in parallel with the transactionhandler (103) providing the response (206) to the authorization request(202).

In one embodiment, the point of interaction (107) receives the message(204) from the media controller (115) in real-time with the transactionhandler (103) processing the authorization request (202) and providingthe authorization response (206).

In one embodiment, the message (204) is arranged to arrive at the pointof interaction (107) in the context of the authorization response (206)provided from the transaction handler (103) to the transaction terminal(105). For example, in one embodiment, the real-time message (204) is toarrive at the point of interaction (107) substantially at the same timethat the authorization response (206), responding to the authorizationrequest (202), arrives at the transaction terminal (105), or with adelay not long enough to cause the user (101) to have the impressionthat the message (204) is in response to an action other than thepayment transaction conducted at the transaction terminal (105). Forexample, in one embodiment, the real-time message (204) is arranged toarrive at the point of interaction (107) prior to the user (101)completing the transaction and leaving the transaction terminal (105),or prior to the user (101) leaving the retail location of the merchantoperating the transaction terminal (105).

In FIG. 9, the system further includes a portal (143) to provideservices to merchants and/or the user (101). In one embodiment,different portals (143) are used to service merchants and users (101).For example, in one embodiment, a merchant portal (143) is used toprovide services to merchants; a different user portal (143) is used toprovide services to users (101). Alternatively, a same portal (143) maybe used to service both merchants and users (101).

For example, in one embodiment, the merchant portal (143) is configuredto allow the user (101) to register the communication reference (205) inassociation with the account data (111), such as the account information(142) of the consumer account (146); and the media controller (115) isto use the communication reference (205) to deliver the message to thepoint of interaction (107). Examples of the communication reference(205) include a mobile phone number, an email address, a user identifierof an instant messaging system, an IP address, etc. In one embodiment,the media controller (115) is configured to transmit the message (204)to the point of interaction (107) of the user (101) using thecommunication reference (205), when the message (204) is responsive toan authorization request (202) identifying the account data (111).

In one embodiment, the user portal (143) allows merchants and/or otherparties to define rules (203) to provide offers (186) as real-timeresponses (e.g., 204) to authorization requests (e.g., 202); and basedon the offer rules (203), the message broker (201) is configured togenerate, or instruct the media controller (115) to generate, real-timemessages (e.g., 204) to provide the offers (186) to the users (e.g.,101).

In one embodiment, the offers (186) can be provided to the users (e.g.,101) and associated with the account data (111) of the users (e.g., 101)via other media channels, such as a search engine, a news website, asocial networking site, a communication application, etc. Examples oftechniques to associate offers with account data (111) can be found inU.S. patent application Ser. No. 12/849,801, filed Aug. 3, 2010 andentitled “Systems and Methods for Multi-Channel Offer Redemption,” thedisclosure of which is hereby incorporated herein by reference.

In one embodiment, the offer (186) includes the benefit of a discount,an incentive, a reward, a rebate, a gift, or other benefit, which can beredeemed upon the satisfaction of certain conditions required by theoffer rules (203). In one embodiment, the real-time message (204) isconfigured to inform the user (101) of the benefit that the user (101)is entitled to upon the completion of the payment associated with theauthorization request (202).

In one embodiment, based on the offer rules (203), the message broker(201) configures a message (204) by selecting the appropriate messagetemplate from (an) existing message(s) template(s), and inserts anyrelevant data (e.g., the communication reference (205)) into theselected template, then passes the configured message to the mediacontroller (115), which delivers the message (204) to the point ofinteraction (107) using the communication reference (205) associatedwith the account data (111) of the user (101).

In one embodiment, the message broker (201) (or a subsystem) isconfigured to manage message templates along with the rules (203) forselecting the appropriate message template from among several potentialchoices.

In one embodiment, the offer rules (203) include offer details,targeting rules, advertisement campaign details, profile mapping,creative mapping, qualification rules, award/notification/fulfillmentrules, approvals, etc. Creative elements for offers include text,images, channels, approvals, etc.

For example, in one embodiment, the offer details specify the benefitsthe user (101) is entitled to when the conditions specified in thefulfillment rules (203) are satisfied.

For example, in one embodiment, the creative mapping specifies thecontent elements that are used to generate a message that describes oridentifies the offer (186), such as the logo of a sponsor of the offer(186), a banner of the offer (186), a message of the offer (186), etc.

For example, in one embodiment, the targeting rules and theadvertisement campaign details specify the way the offer (186) can bedistributed and the requirements of the recipients of the offer (186).In one embodiment, the portal (143) allows the merchants to target theoffers (186) at users (e.g., 101) according to the transaction profiles(127) of the respective users. Some details and examples about thetransaction profiles (127) in one embodiment are provided in the sectionentitled “AGGREGATED SPENDING PROFILE.”

In one embodiment, when the offer rules (203) are activated by themerchant or advertiser via the portal (143), the message broker (201) isconfigured to generate trigger records (207) for the transaction handler(103). The transaction handler (103) is configured to monitor theincoming authorization requests (e.g., 202) to identify requests thatsatisfy the conditions specified in the trigger records (207), duringthe process of the authorization requests (e.g., 202). In oneembodiment, the transaction handler (103) is configured to provide theinformation about the requests (e.g., 202) identified according to thetrigger records (e.g., 207) to the message broker (201) for thetransmission of an appropriate real-time message (e.g., 204) inaccordance with the offer rules (203).

In one embodiment, the conditions specified in a trigger record (e.g.,207) to select a transaction associated with an authorization request(202) for further processing by the message broker (201) is a subset ofconditions required for the generation of the real-time message (204).Once the transaction associated with the authorization request (202) isidentified by the transaction handler (103) according to the triggerrecords (207), the message broker (201) is configured to furtherdetermine whether and/or how to generate the real-time message (204). Inone embodiment, the trigger record (207) identifies the respective offer(186) and/or its associated offer rules (203) to allow the messagebroker (201) to further process the generation and/or transmission ofthe real-time message (204).

For example, in one embodiment, the message broker (201) is configuredto determine whether the user (101) is entitled to the benefit of theoffer (186) if the payment associated with the authorization request(202) is eventually settled. Based on the result of such adetermination, the message broker (201) determines whether or not togenerate the message (204). For example, if the payment transaction isnot actually relevant to the offer (186) (e.g., the conclusion accordingto the trigger record (207) is a false positive), the message broker(201) does not generate the real-time message (204). For example, whenthe payment transaction, if completed, brings the user (101) closer tothe qualification of the benefit redemption associated with the offer(186), the message broker (201) is configured to generate the real-timemessage (204) to indicate the milestone achieved towards the redemptionof the offer (186). For example, when the payment transaction, ifcompleted, brings the user (101) to a point that the user (101) isentitled to the benefit of the offer (186), the real-time message (204)is configured to notify the user (101) of the benefit available upon thesettlement of the payment transaction.

In one embodiment, through the arrangement of including a portion of theconditions in the trigger record (207) and using the message broker(201) to process the remaining conditions, the load applied on thetransaction handler (103) for the detection of transactions of interestto the message broker (201) is reduced. Thus, the impact on theperformance of the transaction handler (103) in processing authorizationrequest (202) in aspects not related to the offer (186) and thereal-time message (204) is reduced.

In one embodiment, a set of standardized types of conditions areidentified for generation of the trigger records (207). The standardizedtypes of conditions are selected to optimize the performance thetransaction handler (103), while reducing the likelihood of falsepositives which are identified by applying the complete set ofconditions associated with the offer rules (203). In generating thetrigger records (207), the conditions in the offer rules (203) aremapped to the standardized types of conditions in a way to eliminatefalse negatives and reduce false positives. In one embodiment, a falsenegative occurs when a conclusion in accordance with the conditionsspecified in the trigger record (207) indicates that the transaction isof no interest to the message broker (201), while a conclusion inaccordance with the conditions specified in the offer rules (203)indicates that the transaction is of interest to the message broker(201); and a false positive occurs when a conclusion in accordance withthe conditions specified in the trigger record (207) indicates that thetransaction is of interest to the message broker (201), while aconclusion in accordance with the conditions specified in the offerrules (203) indicates that the transaction is of no interest to themessage broker (201).

In one embodiment, the generation of the trigger records (207) for thetransaction handler (103) is in real-time with the merchant oradvertiser activating the offer rules (203). Thus, the offer rules (203)can be activated and used for the detection of the new authorizationrequests in real-time, while the transaction handler (103) continues toprocess the incoming authorization requests.

In one embodiment, the portal (143) provides information about thespending behaviors reflected in the transaction data (109) to assist themerchants or advertisers to target offers or advertisements. Forexample, in one embodiment, the portal (143) allows merchants to targetthe offers (186) based on transaction profiles (127). For example, theoffer rules (203) are partially based on the values in a transactionprofile (127), such as an aggregated spending profile (341). In oneembodiment, the offer rules (203) are partially based on the informationabout the last purchase of the user (101) from the merchant operatingthe transaction terminal (105) (or another merchant), and/or theinformation about the location of the user (101), such as the locationdetermined based on the location of the transaction terminal (105)and/or the location of the merchant operating the transaction terminal(105).

In one embodiment, the portal (143) provides transaction basedstatistics, such as merchant benchmarking statistics, industry/marketsegmentation, etc., to assist merchants and advertisers to identifycustomers. In one embodiment, the transaction based statistics areprovided in a way that prevents the merchant from identifying anyspecific individual user (101) associated with the transaction basedstatistics (e.g., to protect the privacy of the individual user (101)).

In one embodiment, the portal (143) is also communicatively coupled to aroute provider (140) that can provide non-transaction data such asusers' location information. In an embodiment, the route provider (140)can be a software, hardware, or firmware (or combinations thereof)system, process or functionality, or component thereof, that performs orfacilitates the processes, features, and/or functions described herein(with or without human interaction or augmentation). The route provider(140) can comprise sub-modules. In an embodiment, location information(144) can be unique to a consumer associated with a consumer account(146). The location information (144) can be recorded in the datawarehouse (149) along with other consumer and transaction data forfurther analysis which can provide monetization opportunities such asproviding coupons or making offers to consumers based on their existingor predicted locations in accordance with embodiments described furtherherein.

In one embodiment, the real-time messages (204) include offers (186)provided according to the offer rules (203) based on predicted futureuser locations and are used to influence customer behaviors while thecustomers are in the purchase mode.

In one embodiment, the benefit of the offers (186) can be redeemed viathe transaction handler (103). The redemption of the offer (186) may ormay not require the purchase details (e.g., SKU level purchase details).Details in one embodiment about redeeming offers (186) via thetransaction handler (103) are provided in U.S. patent application Ser.No. 13/113,710, filed May 23, 2011 and entitled “Systems and Methods forRedemption of Offers,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, when the authorization request (202) for a purchaseindicates that the purchase qualifies the offer (186) for redemption ifthe purchase corresponding to the authorization request (202) iscompleted, the message broker (201) is to construct a message (204) anduse the media controller (115) to deliver the message (204) in real-timewith the processing of the authorization request (202) to the point ofinteraction (107). For example, in one embodiment, the message (204) isconfigured to inform the user (101) that when the purchase is completed,the transaction handler (103) and/or the issuer processor (145) is toprovide the benefit of the offer (186) to the user (101) via statementcredit or some other settlement value, such as points in a registeredloyalty program, or credit at the point of sale using a digital coupondelivered to the user (101) via cell phone.

In one embodiment, the settlement of the payment transactioncorresponding to the authorization request (202) does not occur inreal-time with the processing of the authorization request (202). Forexample, the merchant may submit the complete purchases for settlementat the end of the day, or in accordance with a predetermined schedule.The settlement may occur one or more days after the processing of theauthorization request (202).

In one embodiment, when transactions are settled, the settledtransactions are matched to the authorization requests (202) to identifyoffers (186) that are redeemable in view of the settlement. When theoffer (186) is confirmed to be redeemable based on a record ofsuccessful settlement, the message broker (201) is to use the mediacontroller (115) to provide a message (204) to the point of interaction(107) of the user (101), such as the mobile phone of the user (101). Inone embodiment, the message (204) is to inform the user (101) of thebenefit to be provided as statement credits and/or to provide additionaloffers. In one embodiment, the message (204) to confirm the statementcredits is issued in real-time with the completion of the transactionsettlement.

In one embodiment, the message broker (201) is configured to determinethe identity of the merchant based on the information included in theauthorization request (202) transmitted from the transaction terminal(105) to the transaction handler (103). In one embodiment, the identityof the merchant is normalized to allow the application of the offerrules (203) that are merchant specific.

In one embodiment, the portal (143) is configured to provide datainsight to merchants and/or advertisers. For example, the portal (143)can provide the transaction profile (127) of the user (101), audiencesegmentation information, etc.

In one embodiment, the portal (143) is configured to allow the merchantsand/or advertisers to define and manage offers (186) for their creation,fulfillment and/or delivery in messages (204).

In one embodiment, the portal (143) is configured to allow the merchantsand/or advertisers to test, run and/or monitor the offers (186) fortheir creation, fulfillment and/or delivery in messages.

In one embodiment, the portal (143) is configured to provide reports andanalytics regarding the offers (186).

In one embodiment, the portal (143) is configured to provide operationfacilities, such as onboarding, contact management, certification, filemanagement, workflow, etc. to assist the merchants and/or advertisers tocomplete the tasks related to the offers (186).

In one embodiment, the portal (143) allows the user (101) to opt in oropt out of the real-time message delivery service.

In one embodiment, the portal (143) is configured to present a userinterface that allows an advertiser or merchant to select an offerfulfillment method from a list of options, such as statement credits,points, gift cards, e-certificates, third party fulfillment, etc.

In one embodiment, the merchant or advertiser is to use the pre-computed(“off the rack”) transaction profiles (127) available in the datawarehouse (149) to target the delivery of the offers (186). In oneembodiment, the portal (143) is configured to further allow the merchantor advertiser to edit parameters (e.g., define new parameters based onexisting parameters defined in the pre-computed transaction files (127)to generate new parameters) to customize the generation of thetransaction profiles (127) and/or develop custom transaction profilesfrom scratch.

In one embodiment, the portal (143) is configured to provide avisualization tool to allow the user of the portal (143) (e.g., amerchant or an advertiser) to see clusters of data based on geographicalcodes to identify locations (e.g., GeoCodes), proximity, transactionvolumes, spending patterns, zip codes, customers, stores, etc.

In one embodiment, the portal (143) is configured to provide a userinterface that allows a merchant or advertiser to define cells fortargeting the customers who reside in the cells, based on date/time,profile attributes, map to offer/channel/creative, condition testing,etc.

In one embodiment, the portal (143) is configured to provide a userinterface that allows a merchant or advertiser to monitor the health ofthe system (e.g., the condition of servers, files received or sent,errors, status), monitor the throughput by date or range, by program, bycampaign, or by global view, and monitor aspects of currentprograms/offers/campaigns, such as offer details, package audit reports,etc. In one embodiment, the portal (143) is configured to provide a userinterface to provide reports on topics such as analytics and metricsrelating to lift, conversion, category differentials (e.g., spendingpatterns, transaction volumes, peer groups), with the reportingperformed for a specific program, campaign, cell, GeoCode, proximity,ad-hoc, auditing, etc.

FIG. 10 shows a method to provide real-time messages according to oneembodiment. In FIG. 10, a computing apparatus is configured to generate(211) a trigger record (207) for a transaction handler (103) to identifyan authorization request (202) that satisfies the conditions specifiedin the trigger record (207), receive (213) from the transaction handler(103) information about the authorization request (202) in real-timewith the transaction handler (103) providing a response (206) to theauthorization request (202) to a transaction terminal (105), identify(215) a communication reference (205) of a user (101) associated withthe authorization request (202), determine (217) a message (204) for theuser (101) responsive to the authorization request (202), and provide(219) the message (204) to the user (101) at a point of interaction(107) via the communication reference (205), in parallel with theresponse (206) from the transaction handler (103) to the transactionterminal (105).

In one embodiment, the computing apparatus includes at least one of: atransaction handler (103), a message broker (201), a media controller(115), a portal (143) and a data warehouse (149).

Route Prediction

The lack of ability to target offers based on predicted travel route ofa user is a problem that exists for advertisement networks. Embodimentsdisclosed herein predict the future location of a user based onpredictions of the route that the user might take (e.g., by using GPSbased services) to target RTM/offer activities based on the futurebehavior/location/position. The method operates by indexing routes usedby the user in the past to generate a “dictionary”, which may be furtheraugmented with contextual data. Techniques of text prediction, termfrequency for dictionary scores and/or other language processingtechniques are used to predict the future route of the user. As a resultof the system, advertisement networks can predict the location of theuser and provide more relevant offers/content at a more relevant time.

In one embodiment, non-transaction data used to send real-time messages(204) can include a predicted point of interaction (107). The point ofinteraction (107) can be predicted based on a route a user (101) islikely to traverse. In the world of location based services, if it isdetermined where a user (101) will be, the services offered to the usercan be appropriately targeted. By predicting the route that a user (101)might take, (e.g., by using GPS based services), one can predict theuser's (101) future location or point of interaction (107) and cantarget activities based on that future behavior/position. FIG. 11 showsa schematic diagram wherein a route provider (140) interacts with alocation provider (1102) associated with a user (101). In an embodiment,the route provider (140) obtains the location information (144) of theuser (101) from the location provider (1102) associated with the user(101). In an embodiment, the location provider (1102) can comprise anyequipment that provides GPS data of the user, such as, a GPS device or asmartphone or a device which includes a IP address that may be mapped tothe user's location. Accordingly, a starting location (1104) and aninitial portion of the route traversed by the user (101) is determinedby the route provider (140) which can then predict the likely route thatcan be taken by the user (101) and consequently the likely destinationof the user (101) can be derived in accordance with embodiments thatwill be described in further detail herein. This facilitates the portal(143) to provide location-based services such as, real-time messagesrelated to deals, coupons or other alerts to the user (101).

FIG. 12 shows a schematic diagram of a route provider (140) inaccordance with an embodiment. The route provider (140) can be, forexample, a data processing system (170) that is configured to predict aroute (1252) that a user (101) will likely employ and communicate thepredicted route (1252) to the portal (143). In an embodiment, the routeprovider (140) which can be comprised in the memory (167), includes alocation information receiver (1242), a route dictionary builder (1244),a route predictor (1246) and a route information transmitter (1248). Thelocation information receiver (1242) is configured to receive locationinformation of consumers associated with the consumer accounts. In anembodiment, the location information receiver (1242) can receive the GPSinformation or other network information, indicative of location of theconsumers. In one embodiment, a consumer or a user (101) can besolicited to provide access to their location information (144) in orderto receive location-based services. For example, when a user (101)consents to the collection of his or her location information (144), theuser (101) can be requested to provide their mobile number or otherinformation that can aid collection of location information (144). Thisenables obtaining location information (144) from the consumer's mobilephone and location-based services appropriate to the consumer's locationand context can be provided in real-time, for example, via the mobilephone. In an embodiment, the location information receiver (1242) canreceive location information of a user's starting location (1104) and/oran initial portion of the route traversed by the user (101) tofacilitate generating the route prediction (1252).

The location information (144) collected by the location informationreceiver (1242) is provided to a route dictionary builder (1244) thatbuilds and updates a database or a dictionary (1150) of a user's routeswherein each route is coded into a collection of identifiers such as,letters which form a term or a word in the route dictionary (1150). Inan embodiment, the contents of the route dictionary (1150) are accessedby the route predictor (1246) to predict a route (1252) that is likelyto be travelled by the user (101). This is facilitated by employinglanguage processing techniques in accordance with embodiments describedfurther herein to analyze contents of the route dictionary (1150) forthe route prediction (1252). In an embodiment, context data which canalso be recorded in the route dictionary (1150) can be employed for theroute prediction (1252). In an embodiment, context data from externalsources (not shown) in conjunction with the route dictionary (1150)contents can also be employed for making route prediction (1252). Theroute selected by the route predictor (1246) is provided to a routeinformation transmitter (1248) which transmits the route prediction(1252) to a requestor. In an embodiment, the requestor can be the portal(143) which is configured to provide location-based services. In anembodiment, the requestor can be a third-party requestor, such as, amerchant or another intermediate entity that logs request for the routeprediction (1252).

FIG. 13 shows a method of predicting a route and updating the routedictionary (1150) by a computing apparatus according to one embodiment.Initially, a portion of the route traversed by the user (101) isreceived (1302). A route dictionary (1150) associated with the user isaccessed (1304) and a partial term that encodes the portion of the routetraversed by the user is retrieved (1306). The user's (101) likely routeis predicted (1308) based at least on the partial term which indicates aportion of the route already traversed by the user (101). In a furtherembodiment the route dictionary (1150) can be updated by observing andrecording user behavior. Thus, the computing apparatus that isconfigured to provide route prediction (1252) can be further configuredto update the route dictionary (1150) by monitoring the actual routetraversed by the user (101) and recording the accuracy of the routeprediction (1252). In one embodiment, the route traversed by the user(101) is monitored (1310) by the computing apparatus. In an embodiment,the computing apparatus updates the route dictionary (1150) with theroute taken by the user (101) at a later time according to the proceduredetailed herein. The computing apparatus determines (1312) if the user(101) employed the predicted route (1252). If it is determined that theuser (101) employed the predicted route (1252), the count of thepredicted route (1252) is updated (1314). If it is determined that theuser (101) did not employ the predicted route (1252), then adetermination is further made (1316) if the user (101) traversed a routealready recorded or currently existing in the route dictionary (1150).If the route traversed by the user already exists in the routedictionary (1150), the count of the existing route is updated (1320). Ifthe route taken by the user (101) does not exist in the route dictionary(1150), the new route information is recorded (1318) in the routedictionary (1150) with the frequency count set to 1 (1322).Subsequently, as the user (101) employs the newly recorded route, itsfrequency count in the route dictionary (1150) can be updatedaccordingly. In an embodiment, if a route is not used for longer than apreset threshold time, the route can be deleted from the routedictionary (1150) thereby optimizing usage of resources and the routeprediction process. The computing apparatus can therefore be configuredto not only predict routes but also to observe the accuracy of routepredictions and learn from them so that the accuracy of the routepredictions can be increased with increasing observations regarding userbehavior.

FIG. 14 is a map showing various routes to frequent destinations of auser (101) who employs a routing device (1102) wherein each intersectionof two routes is encoded as a vertex defined in terms of a Cartesiancoordinate system by a computing apparatus executing the routedictionary builder (1244). In an embodiment, the routing device (1102)can be a smartphone that provides routing or location information viaGPS (Global Positioning System) or via other networks such as Wi-finetworks. Accordingly, the starting location (1404) of the user (101) oran initial portion of the route traversed by the user (101) can bedetected by the route provider (140) and hence the portal (143). In anembodiment, the starting location (1404) can be a location predefinedwithin the route dictionary (1150) as the residence of the user (101).In an embodiment, the starting location (1404) is a location that can beconfigured by the user (101). For example, if the user (101) is visitinganother city, the user (101) can configure his/her temporary place ofstay as the starting location (1404). In an embodiment, the startinglocation (1404) can be automatically configured by recording andanalyzing user behavior. For example, if it is determined that the user(101) most frequently begins location information exchange from aparticular location, such a location can be determined as the startinglocation (1404). Each of the routes to the destinations frequented bythe user is analyzed by the route dictionary builder (1244) from theuser's (101) residence (1404) in order to identify vertices on theroutes. In an embodiment, the vertices on the routes are located at theintersection of two routes and can be coded in terms of Cartesiancoordinates with the origin located at the starting location or theuser's residence (1404). In an embodiment, the vertices can be locatedat turning points along the route where the user (101) changes thedirection of travel. Accordingly, the vertices (1452) and (1468) at theconvenience store (1406) are encoded as (0, 2) and (1, 2), the vertex(1454) at the first gas station (1408) is encoded as (1, 3), thevertices (1456) and (1458) at the outdoor mall (1410) and a second gasstation (1412) respectively are encoded as (3, 3) and (3, 4), a vertex(1460) at a third gas station (1414) is encoded as (5, 2), a vertex(1462) at a factory (1416) which can be a place of work of the user(101) is encoded as (7, 4). Additionally a vertex (1464) located midwaybetween the second gas station (1412) and the factory (1416) is encodedas (5, 4) and another vertex (1466) located between the conveniencestore (1406) and the third gas station (1414) is encoded as (3, 2). Inthis embodiment, the vertices are defined in terms of their distancesalong the horizontal and vertical axes from the origin (1472) based onarbitrarily defined distance units. For example, the distances of allthe locations along the two axes can be defined or encoded as multiplesof the length of the street (1470) extending between the origin (1472)(0, 0) and the vertex (1, 0) (1474). In an embodiment, the coordinatescan be based on standardized distance units such as the SI units.

FIG. 15 shows a map wherein each route segment between two vertices isdefined or encoded in the route dictionary (1150) in terms of thevertices by the route dictionary builder (1244). Thus, a route segmentbetween the starting location (1404) and the convenience store (1406) isdefined in terms of four numbers (0, 0, 0, 2) which are the coordinatesof the two vertices (0, 0) and (0, 2) located at the two ends of theroute segment. Similarly other route segments are encoded or defined interms of the coordinates associated with the two vertices located at theends of each route segment. However, defining each route segment interms of four numbers can lead to complicated number combinations whencombining different route segments to form a route. Moreover, whiletreating numbers as a form of text data can permit a limited applicationof text processing techniques, the processing can be further simplifiedor more extensive text processing techniques can be applied by encodingroute segments as normal text data in accordance with embodimentsdescribed in further detail herein.

FIG. 16 shows a map wherein different route segments associated withvarious vertices are further assigned arbitrary identifiers, such asletters of a language, by the route dictionary builder (1244).Therefore, each route in the route dictionary is represented as a termthat is formed from the collection of identifiers arranged to representthe arrangement of the route segments within the route. The route fromthe starting location (1404) and the convenience store (1406) defined interms of (0, 0, 0, 2) in FIG. 15 is assigned letter ‘B’. Similarly routesegment (0, 0, 1, 0) is assigned letter ‘A’, route segment (1, 0, 1, 2)is assigned letter ‘C’, route segment (0, 2, 1, 2) is assigned letter‘D’, route segment (1, 2, 3, 2) is assigned letter ‘E’, route segment(3, 2, 5, 2) is assigned letter ‘F’, route segment (1, 2, 1, 3) isassigned letter ‘G, route segment (3, 2, 2, 3) is assigned letter ‘H’,route segment (5, 2, 5, 4) is assigned letter ‘I’, route segment (1, 3,3, 3) is assigned letter ‘J’, route segment (3, 3, 3, 4) is assignedletter ‘K’, route segment (3, 4, 5, 4) is assigned letter ‘L’ and routesegment (5, 4, 7, 4) is assigned letter ‘M’. It may be appreciated thatthe foregoing letter assignments are detailed only by the way ofillustration and not limitation and that other arbitrary assignment ofletters can be contemplated according to other embodiments. In fact,routes in the route dictionary (1150) can be encoded in a language otherthan English by assigning letters from a non-English language and thetext processing techniques that are currently know or which are to beinvented in the non-English language can be implemented to build and usea non-English language route dictionary in accordance with embodimentsdescribed herein. It may be appreciated that as there are a limitednumber of alphabets/letters in any language, all the routes frequentedby a user may not necessarily be encoded in the route dictionary (1150).Hence, the top ‘N’ routes (N being a natural number) frequentlytraversed by a user can be selected for encoding in accordance withembodiments described herein. In an embodiment, the top ‘N’ routes maybe selected based not only on the user data but also on otherconsiderations such as location of commercial establishments like theconvenience store (1406), the outdoor mall (1410), gas station (1408)etc., along the route. For example, if the user's everyday routeconnecting home and work passes predominantly through an area with a fewor no commercial locations, such as a residential area, then such routemay not be encoded in the route diction (1150) even if traversed dailyby the user. It may be further appreciated that the route dictionary(1150) that comprises the encoding of a user's (101) route with theassignment of vertices and arbitrary identifiers is unique to a user. Inan embodiment, another user who uses that same route or a route thatpartially overlaps or intersects with the route illustrated in FIG. 15may have his/her route encoded with different identifiers in his/herrespective route dictionary even for those overlapping/intersectingportions of the routes.

FIG. 17 illustrates a route 1702 starting from the user's residence(1404) and ending at the factory (1416) encoded as a word in the routedictionary (1150) by the route dictionary builder (1144) in accordancewith an embodiment of the present disclosure. In an embodiment, theroute can comprise another partial route. Alternately, a route can be acombination of two or more partial routes. For example, a route (1704)from the starting location or the user's residence (1404) to the outdoormall (1410) is a combination of route segments B, D, G and J. Therefore,it can be encoded as for example, the word ‘BDGJ’ or ‘bdgj’ or ‘Bdgj’ orother identical combinations of the letters regardless of theircapitalization. In addition, the route (1706) from the outdoor mall(1410) to the factory (1416) can be encoded as ‘klm’, ‘KLM’ or ‘Klm’.The route (1702) from the user's residence (1404) to the factory (1416)can be accordingly encoded as ‘BDGJKLM’ or ‘Bdgjklm’ or ‘bdgjklm’ whichis a combination of the two routes from the user's residence (1404) tothe outdoor mall (1410) ‘bdgj’ and the outdoor mall (1410) to thefactory (1416) ‘klm’.

FIG. 18 is a flowchart 1800 detailing a method of generating the routedictionary (1150) by the route dictionary builder (1244) by tracking theuser's route. The method begins with a receiving route data (1802) ofthe user (101) in accordance with embodiments described herein. Theroute information can be received via various devices employed by theuser (101). In an embodiment, route information can be initiallyreceived, recorded and subsequently analyzed after the user (101) hasreached the destination. In an embodiment, a starting point or astarting location of the route is identified and an origin of acoordinate system which is used to encode the route is assigned to thestarting location (1804). For example, for a new user, the startingpoint or starting location (1405) can be predetermined within the routedictionary builder (1244) as a residence of the new user. In anembodiment, the starting point or starting location (1405) can be alocation sensed from the user equipment such as the routing device(1402). The route traversed by the user (101) to reach the destinationis analyzed and vertices of the route are identified (1806). In anembodiment, the vertices on the route can be located at the intersectionof two streets with different names. In an embodiment, the vertices canbe located at left or right turns along the route where the user (101)changes the direction of travel. Route segments are then defined (1808)based on the vertices. A portion of the user's route between twoconsecutive vertices can be recorded or identified as a unique routesegment in the user's route dictionary (1205). Each identified routesegment is assigned (1810) a letter based for example, on its length anda collection of letters representing route segments that constitute aroute from one location to another along the user's path are recorded(1812) as words in the user's route dictionary (1205). Hence, two routesegments with similar lengths can be identified with the same letter inthe route dictionary. For each route and its associated word, the routedictionary builder (1244) also records and updates (1814) a frequencycount of the word each time the user (101) takes the route. In anembodiment, a context can be also associated with a route/word in theroute dictionary (1150) in accordance with embodiments described furtherherein.

FIG. 19 is an illustration showing example entries in a route dictionary(1150) which encodes each of the routes as a collection of identifiersthat form a term or a word. The route dictionary (1150) records a startpoint (1902) for each route, a term or word (1904) for each route andthe frequency (1906) with which the user (101) employs each route. Basedon the entries shown, it can be determined that the user (101) employsthe route from the residence (1405) to the factory (1416) associatedwith the word ‘Bdgjklm’ most frequently. Similarly, the reverse routefrom the factory (1416) to the residence (1405) associated with the word‘mlkjgdb’ has equal frequency. Text processing techniques can now beapplied to analyze the data in the route dictionary (1150) to make routepredictions.

FIG. 20 is an illustration (2000) showing an example of updating countof a route traversed by the user (101) by route pruning. The techniquesof pruning a route can be employed to keep consistent representations ofroutes in the dictionary when minor deviations occur. The illustrationshows that the user (101) traversed route ‘Bdcabdgjk’. In an embodiment,the route ‘Bdcabdgjk’ can be analyzed using text processing techniquessuch as, pattern matching, to identify the known route ‘bdgjk’. Thepattern ‘bcda’ can be pruned from the original route ‘Bdcabdgjk’ asbeing a minor deviation and the frequency count of ‘bdgjk’ is updated.Thus, text analysis can be applied to analyze words or routes includingminor deviations to find known terms to update their frequencies. In anembodiment, a new entry ‘Bdcabdgjk’ can be generated in the routedictionary (1150) for the user (101).

FIG. 21 is a map (2100) showing an example route prediction from theroute dictionary (1150) entries shown in FIG. 19. The example showsprediction of a route or destination of the user (101) when it is knownthat the user travelled along the route ‘Bdg’. According to the entriesof the route dictionary (1150), it is possible that the user (101) willtraverse route ‘Bdgj’ and reach the outdoor mall (1410) or the user(101) can continue onwards and to travel along ‘Bdgjklm’ and reach thefactory (1416). By applying text processing techniques to the data fromthe route dictionary (1150), knowing a partial term ‘Bdg’ a likelihoodof occurrence of a word comprising the known partial term is calculatedas:Word frequency(Anticipated route)=term frequency/dictionary frequency

Based on the above calculation, knowing that the user is en route ‘Bdg’the likelihood that the user (101) will stop at the outdoor mall (1410)is:Anticipated route Bdgj=3/13=0.230

Similarly, knowing that the user has traversed route ‘Bdg’ thelikelihood that the user will reach the factory (1416) is:Anticipated route ‘Bdgjklm’=10/13=0.769

Based on the calculations, the route predictor (1246) outputs theconclusion that the expected route of the user is ‘Bdgjklm’. In anembodiment, this conclusion is fed by the route provider (140) to theportal (143) which can employ further data about the user (101) from thedata warehouse (149) to send real time messages to the user (101)including discounts, coupons or alerts associated with particularlocations along the route ‘Bdgjklm’ in accordance with embodimentsdescribed, for example, at sections “Matching advertisement andtransaction” or “Coupon matching”. Thus, based for example, on thepredicted route, coupons, promotions or other real-time messagesassociated with the outdoor mall 1410 or the convenience store 1406 canbe selectively forwarded to the user as opposed to coupons, promotionsor other real-time messages associated with the gas station 1414. Thisis because the predicted route ‘Bdgjklm’ is proximate to the outdoormall 1410 or the convenience store 1406 but lies further away from thegas station 1414.

FIG. 22 shows an example embodiment (2200) of a route dictionary (1150)including route context information. In addition to encoded routeinformation, an embodiment of the route dictionary (1150) can comprisecontext information associated with each route. In an embodiment, thecontext information (2202) can comprise time, day, date or combinationsthereof at which the user (101) takes each route. Therefore, the contextinformation can be considered in addition to the frequency count forgenerating a more accurate route prediction.

FIG. 23 illustrates a map 2300 that shows route prediction based oncontext. In an embodiment, based on the knowledge that the user hastraversed partial route ‘ml’, two possibilities exist for routeprediction from the data included in the route dictionary (1150). It ispossible that the user is on the way to the outdoor mall (1410) andhence the route the user (101) would traverse is ‘mlk’. It is alsopossible that the user will not stop at the outdoor mall (1416) and willinstead proceed all the way to the residence/starting point (1404) inwhich case the user (101) will take the route ‘mlkjgdB’. Withoutreference to the context data, based only on the frequency count it canbe predicted that the likelihood of the user taking route ‘mlk’ is about16% whereas the likelihood of the user (101) taking the route ‘mlkjgdB’is 83%. Therefore, it can be concluded that the expected route of theuser (101) is ‘mlkjgdB’. In an embodiment, context data can be appliedto further refine the predictions. For example, based on context datasuch as the time log data it can be concluded that when the user (101)employs route segments ‘Bd’ between 8.00 AM to 10.00 AM, the likelihoodthat the user (101) will continue on to ‘Bdgjklm’ to reach the factory(1416) is very high compared to the likelihood of the user taking‘Bdehk’ to the outdoor mall (1410). Conversely, when the user (101)travels along the route segments ‘Bd’ between 2.00 PM-4.00 PM it iscertain that the user (101) is proceeding to the outdoor mall (1410) via‘Bdehk’ and not to the factory (1416). Thus, employing contextinformation can further refine route predictions. In an embodiment, thecontext information need not be recorded in the route dictionary (1150).Rather, the route provider (140) can be configured to obtain contextinformation directly from external sources or via the portal (143) andprovide route predictions based on such external context information.

FIG. 24 shows a flowchart 2400 that details a method of providing routepredictions in accordance with embodiments described herein. In oneembodiment, the process of predicting route can begin with receiving apartial term (2402) that indicates a portion of a route alreadytraversed by the user (101) who is travelling towards a destination.Additional context information either recorded in the route dictionary(1150) and/or obtained from external sources (not shown) is received(2404). For example, time of the day at which the route prediction isrequested can be used as context information. Other context informationsuch as, day of the week also be associated with the data in the routedictionary (1150). All the words in the route dictionary (1150) thatinclude the partial term and satisfy the context information areidentified (2406). The frequency of occurrence in the route dictionary(1150) of each of the words is obtained (2408). The sum of thefrequencies of all the words that comprise the partial term and satisfythe context information is also obtained (2410) and the frequency ofeach word is divided by the sum of frequencies to determine a fractionor percentage of occurrence (2412) for each word among all the words.The word with the highest percentage of occurrence is selected as apredicted route (2414).

FIG. 25 shows a method to provide benefits according to one embodiment.In FIG. 25, the computing apparatus is configured to generate (231) atrigger record (207) for a transaction handler (103) to identify anauthorization request (202) that satisfies the conditions specified inthe trigger record (207) for an offer (186) associated with an accountidentifier (e.g., account data (111), account information (142), oraccount number (302)).

In FIG. 25, the computing apparatus is configured to identify (233) theauthorization request (202) of a transaction according to the triggerrecord (207) and determine (235) whether the transaction, if completed,satisfies the conditions required for the qualification of a benefit ofthe offer (186) in accordance with the offer rules (203).

If the transaction satisfies (237) the benefit qualification conditionsin accordance with the offer rules (203) of the offer (186), thecomputing apparatus is configured to transmit (239), to a communicationreference (205) associated with the account identifier (e.g., accountdata (111), account information (142), or account number (302)), amessage (204) to identify the qualification. The computing apparatus isconfigured to further generate (241) a trigger record (207) for thetransaction handler (103) to identify a settlement request for thetransaction. If the transaction is settled (243), the computingapparatus is configured to provide (245) the benefit of the offer (186)to a consumer account (146) identified by the account identifier (e.g.,account data (111), account information (142), or account number (302))via statement credit. In one embodiment, the statement credit isprovided as part of the settlement operations of the transaction.

In one embodiment, a computer-implemented method includes: storing, in acomputing apparatus having a transaction handler (103), a plurality oftrigger records (207); processing, by the transaction handler (103), anauthorization request (202) received from an acquirer processor (147),where the authorization request (202) is processed for a payment to bemade by an issuer processor (145) on behalf of a user (101) having anaccount identifier (e.g., account data (111), account information (142),or account number (302)) associated with the issuer processor (145), andthe acquirer processor (147) is configured to receive the payment onbehalf of a merchant operating the transaction terminal (105).

In one embodiment, the method further includes: determining, by thetransaction handler (103), whether the authorization request (202)matches one of the plurality of trigger records (207) by determiningwhether the attributes of the transaction associated with theauthorization request (202) satisfies the conditions specified in one ofthe plurality of trigger records (207).

In one embodiment, if the authorization request (202) matches a triggerrecord (207) in the plurality of the trigger records (207), thecomputing apparatus is configured to identify a communication reference(205) of the user (101) in accordance with the trigger record (207),generate a message (204) regarding a benefit to be provided to the user(101) upon the completion of the payment, and transmit the message (204)to the user (101) via the communication reference (205) in real-timewith the processing of the authorization request (202). In oneembodiment, the communication reference (205) is one of: a phone numberand an email address; and the message (204) is transmitted via at leastone of: short message service and email.

In one embodiment, the message (204) is transmitted to a mobile phone ofthe user (101) via the communication reference (205).

In one embodiment, the message (204) is transmitted to the user (101)via a communication channel separate from a communication channel usedto provide a response (206) to the authorization request (202).

In one embodiment, the method further includes the computing apparatusidentifying an offer (186) based on transaction data (109) of the user;and the message (204) is configured to provide the offer (186).

In one embodiment, the computing apparatus includes the portal (143)configured to receive offer rules (203) from a merchant for the offer(186); and the offer (186) is identified for delivery in the real-timemessage (204) based further on the offer rules (203).

In one embodiment, the offer (186) is identified in real-time with theprocessing of the authorization request (202), or in response to adetermination that the authorization request (202) matches the triggerrecord (207).

In one embodiment, the offer (186) is identified based on a profile(e.g., 131, or 341) of the user (101). In one embodiment, the profile(e.g., 131 or 341) summarizes the transaction data (109) of the user(101). In one embodiment, the computing apparatus includes the profilegenerator (121) configured to generate the profile (e.g., 341) from thetransaction data (109) of the user (101) via a cluster analysis (329)and a factor analysis (327), as described in the section entitled“AGGREGATED SPENDING PROFILE.”

In one embodiment, the message (204) indicates that a transaction forwhich the authorization request (202) is processed is eligible for thebenefit of an offer (186) associated with the account identifier (e.g.,account data (111)) of the user (101), when the transaction iseventually completed and settled.

In one embodiment, the offer (186) is stored in the data warehouse (149)in association with the account identifier (e.g., account data (111));and the trigger record (207) identifies the offer (186) to allow themessage broker (201) to further check whether the transaction meets thebenefit redemption conditions of the offer (186).

In one embodiment, the computer apparatus is configured to determinewhether the payment, if completed, entitles the user (101) to thebenefit of the offer (186), in response to a determination that theauthorization request (202) matches the trigger record (207); and themessage (204) is transmitted to the user (101) via the communicationreference (205) in response to an indication of the approval of theauthorization request (202) and after a determination is made that thepayment, if completed, entitles the user (101) to the benefit of theoffer (186).

In one embodiment, the transaction handler (103) is configured toidentify a settled transaction corresponding to the authorizationrequest (202) that triggers the message (204), and then provide thebenefit of the offer (186) to the user (101) via statement credits, orloyalty program points, after the settled transaction is identified.

In one embodiment, the transaction handler (103) is configured toprovide the benefit of the offer (186) to the user (101) via point ofsale credit using digital coupons transmitted to cellular telephone ofthe user (101) during the processing of the payment at the transactionterminal (105).

In one embodiment, the transaction handler (103) is configured toprocess a settlement request for the payment and provide the benefit ofthe offer (186) to the user (101) via statement credit to a consumeraccount (146) corresponding to the account identifier (e.g., accountdata (111)) in response to the completion of the settlement of thepayment, or as part of the settlement of the payment.

In one embodiment, the computing apparatus is configured to generate asecond trigger record for the transaction handler (103) to monitor thesettlement of the payment, in order to provided a benefit in response tothe settlement of the payment, or as part of the settlement of thepayment.

In one embodiment, the computing apparatus includes: a data warehouse(149) configured to store a plurality of trigger records (207); atransaction handler (103) coupled with the data warehouse (149) andconfigured to process an authorization request (202) received from anacquirer processor (147); and a message broker (201) coupled with thetransaction handler (103) such that after the transaction handler (103)determines that the authorization request (202) matches a trigger record(207) in the plurality of the trigger records (207), the message broker(201) identifies a communication reference (205) of the user (101) inaccordance with the trigger record (207) and generates a message (204)regarding a benefit to be provided to the user (101) upon completion ofthe payment. The computing apparatus further includes a media controller(115) coupled with the message broker (201) to transmit the message(204) to the user (101) via the communication reference (205) inreal-time with the transaction handler (103) processing theauthorization request (202).

Details about the system in one embodiment are provided in the sectionentitled “SYSTEM,” “CENTRALIZED DATA WAREHOUSE” and “HARDWARE.”

Variations

Some embodiments use more or fewer components than those illustrated inFIGS. 1 and 4-7. For example, in one embodiment, the user specificprofile (131) is used by a search engine to prioritize search results.In one embodiment, the correlator (117) is to correlate transactionswith online activities, such as searching, web browsing, and socialnetworking, instead of or in addition to the user specific advertisementdata (119). In one embodiment, the correlator (117) is to correlatetransactions and/or spending patterns with news announcements, marketchanges, events, natural disasters, etc. In one embodiment, the data tobe correlated by the correlator with the transaction data (109) may notbe personalized via the user specific profile (131) and may not be userspecific. In one embodiment, multiple different devices are used at thepoint of interaction (107) for interaction with the user (101); and someof the devices may not be capable of receiving input from the user(101). In one embodiment, there are transaction terminals (105) toinitiate transactions for a plurality of users (101) with a plurality ofdifferent merchants. In one embodiment, the account information (142) isprovided to the transaction terminal (105) directly (e.g., via phone orInternet) without the use of the account identification device (141).

In one embodiment, at least some of the profile generator (121),correlator (117), profile selector (129), and advertisement selector(133) are controlled by the entity that operates the transaction handler(103). In another embodiment, at least some of the profile generator(121), correlator (117), profile selector (129), and advertisementselector (133) are not controlled by the entity that operates thetransaction handler (103).

For example, in one embodiment, the entity operating the transactionhandler (103) provides the intelligence (e.g., transaction profiles(127) or the user specific profile (131)) for the selection of theadvertisement; and a third party (e.g., a web search engine, apublisher, or a retailer) may present the advertisement in a contextoutside a transaction involving the transaction handler (103) before theadvertisement results in a purchase.

For example, in one embodiment, the customer may interact with the thirdparty at the point of interaction (107); and the entity controlling thetransaction handler (103) may allow the third party to query forintelligence information (e.g., transaction profiles (127), or the userspecific profile (131)) about the customer using the user data (125),thus informing the third party of the intelligence information fortargeting the advertisements, which can be more useful, effective andcompelling to the user (101). For example, the entity operating thetransaction handler (103) may provide the intelligence informationwithout generating, identifying or selecting advertisements; and thethird party receiving the intelligence information may identify, selectand/or present advertisements.

Through the use of the transaction data (109), account data (111),correlation results (123), the context at the point of interaction,and/or other data, relevant and compelling messages or advertisementscan be selected for the customer at the points of interaction (e.g.,107) for targeted advertising. The messages or advertisements are thusdelivered at the optimal time for influencing or reinforcing brandperceptions and revenue-generating behavior. The customers receive theadvertisements in the media channels that they like and/or use mostfrequently.

In one embodiment, the transaction data (109) includes transactionamounts, the identities of the payees (e.g., merchants), and the dateand time of the transactions. The identities of the payees can becorrelated to the businesses, services, products and/or locations of thepayees. For example, the transaction handler (103) maintains a databaseof merchant data, including the merchant locations, businesses,services, products, etc. Thus, the transaction data (109) can be used todetermine the purchase behavior, pattern, preference, tendency,frequency, trend, budget and/or propensity of the customers in relationto various types of businesses, services and/or products and in relationto time.

In one embodiment, the products and/or services purchased by the user(101) are also identified by the information transmitted from themerchants or service providers. Thus, the transaction data (109) mayinclude identification of the individual products and/or services, whichallows the profile generator (121) to generate transaction profiles(127) with fine granularity or resolution. In one embodiment, thegranularity or resolution may be at a level of distinct products andservices that can be purchased (e.g., stock-keeping unit (SKU) level),or category or type of products or services, or vendor of products orservices, etc.

The profile generator (121) may consolidate transaction data for aperson having multiple accounts to derive intelligence information aboutthe person to generate a profile for the person (e.g., transactionprofiles (127), or the user specific profile (131)).

The profile generator (121) may consolidate transaction data for afamily having multiple accounts held by family members to deriveintelligence information about the family to generate a profile for thefamily (e.g., transaction profiles (127), or the user specific profile(131)).

Similarly, the profile generator (121) may consolidate transaction datafor a group of persons, after the group is identified by certaincharacteristics, such as gender, income level, geographical location orregion, preference, characteristics of past purchases (e.g., merchantcategories, purchase types), cluster, propensity, demographics, socialnetworking characteristics (e.g., relationships, preferences, activitieson social networking websites), etc. The consolidated transaction datacan be used to derive intelligence information about the group togenerate a profile for the group (e.g., transaction profiles (127), orthe user specific profile (131)).

In one embodiment, the profile generator (121) may consolidatetransaction data according to the user data (125) to generate a profilespecific to the user data (125).

Since the transaction data (109) are records and history of pastpurchases, the profile generator (121) can derive intelligenceinformation about a customer using an account, a customer using multipleaccounts, a family, a company, or other groups of customers, about whatthe targeted audience is likely to purchase in the future, howfrequently, and their likely budgets for such future purchases.Intelligence information is useful in selecting the advertisements thatare most useful, effective and compelling to the customer, thusincreasing the efficiency and effectiveness of the advertising process.

In one embodiment, the transaction data (109) are enhanced withcorrelation results (123) correlating past advertisements and purchasesresulting at least in part from the advertisements. Thus, theintelligence information can be more accurate in assisting with theselection of the advertisements. The intelligence information may notonly indicate what the audience is likely to purchase, but also howlikely the audience is to be influenced by advertisements for certainpurchases, and the relative effectiveness of different forms ofadvertisements for the audience. Thus, the advertisement selector (133)can select the advertisements to best use the opportunity to communicatewith the audience. Further, the transaction data (109) can be enhancedvia other data elements, such as program enrollment, affinity programs,redemption of reward points (or other types of offers), onlineactivities, such as web searches and web browsing, social networkinginformation, etc., based on the account data (111) and/or other data,such as non-transactional data discussed in U.S. patent application Ser.No. 12/614,603, filed Nov. 9, 2009 and entitled “Analyzing LocalNon-Transactional Data with Transactional Data in Predictive Models,”the disclosure of which is hereby incorporated herein by reference.

In one embodiment, the entity operating the transaction handler (103)provides the intelligence information in real-time as the request forthe intelligence information occurs. In other embodiments, the entityoperating the transaction handler (103) may provide the intelligenceinformation in batch mode. The intelligence information can be deliveredvia online communications (e.g., via an application programminginterface (API) on a website, or other information server), or viaphysical transportation of a computer readable media that stores thedata representing the intelligence information.

In one embodiment, the intelligence information is communicated tovarious entities in the system in a way similar to, and/or in parallelwith the information flow in the transaction system to move money. Thetransaction handler (103) routes the information in the same way itroutes the currency involved in the transactions.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to select items offered on different merchant websitesand store the selected items in a wish list for comparison, reviewing,purchasing, tracking, etc. The information collected via the wish listcan be used to improve the transaction profiles (127) and deriveintelligence on the needs of the user (101); and targeted advertisementscan be delivered to the user (101) via the wish list user interfaceprovided by the portal (143). Examples of user interface systems tomanage wish lists are provided in U.S. patent application Ser. No.12/683,802, filed Jan. 7, 2010 and entitled “System and Method forManaging Items of Interest Selected from Online Merchants,” thedisclosure of which is hereby incorporated herein by reference.

Aggregated Spending Profile

In one embodiment, the characteristics of transaction patterns ofcustomers are profiled via clusters, factors, and/or categories ofpurchases. The transaction data (109) may include transaction records(301); and in one embodiment, an aggregated spending profile (341) isgenerated from the transaction records (301), in a way illustrated inFIG. 2, to summarize the spending behavior reflected in the transactionrecords (301).

In one embodiment, each of the transaction records (301) is for aparticular transaction processed by the transaction handler (103). Eachof the transaction records (301) provides information about theparticular transaction, such as the account number (302) of the consumeraccount (146) used to pay for the purchase, the date (303) (and/or time)of the transaction, the amount (304) of the transaction, the ID (305) ofthe merchant who receives the payment, the category (306) of themerchant, the channel (307) through which the purchase was made, etc.Examples of channels include online, offline in-store, via phone, etc.In one embodiment, the transaction records (301) may further include afield to identify a type of transaction, such as card-present,card-not-present, etc.

In one embodiment, a “card-present” transaction involves physicallypresenting the account identification device (141), such as a financialtransaction card, to the merchant (e.g., via swiping a credit card at aPOS terminal of a merchant); and a “card-not-present” transactioninvolves presenting the account information (142) of the consumeraccount (146) to the merchant to identify the consumer account (146)without physically presenting the account identification device (141) tothe merchant or the transaction terminal (105).

In one embodiment, certain information about the transaction can belooked up in a separate database based on other information recorded forthe transaction. For example, a database may be used to storeinformation about merchants, such as the geographical locations of themerchants, categories of the merchants, etc. Thus, the correspondingmerchant information related to a transaction can be determined usingthe merchant ID (305) recorded for the transaction.

In one embodiment, the transaction records (301) may further includedetails about the products and/or services involved in the purchase. Forexample, a list of items purchased in the transaction may be recordedtogether with the respective purchase prices of the items and/or therespective quantities of the purchased items. The products and/orservices can be identified via stock-keeping unit (SKU) numbers, orproduct category IDs. The purchase details may be stored in a separatedatabase and be looked up based on an identifier of the transaction.

When there is voluminous data representing the transaction records(301), the spending patterns reflected in the transaction records (301)can be difficult to recognize by an ordinary person.

In one embodiment, the voluminous transaction records (301) aresummarized (335) into aggregated spending profiles (e.g., 341) toconcisely present the statistical spending characteristics reflected inthe transaction records (301). The aggregated spending profile (341)uses values derived from statistical analysis to present the statisticalcharacteristics of transaction records (301) of an entity in a way easyto understand by an ordinary person.

In FIG. 2, the transaction records (301) are summarized (335) via factoranalysis (327) to condense the variables (e.g., 313, 315) and viacluster analysis (329) to segregate entities by spending patterns.

In FIG. 2, a set of variables (e.g., 311, 313, 315) are defined based onthe parameters recorded in the transaction records (301). The variables(e.g., 311, 313, and 315) are defined in a way to have meanings easilyunderstood by an ordinary person. For example, variables (311) measurethe aggregated spending in super categories; variables (313) measure thespending frequencies in various areas; and variables (315) measure thespending amounts in various areas. In one embodiment, each of the areasis identified by a merchant category (306) (e.g., as represented by amerchant category code (MCC), a North American Industry ClassificationSystem (NAICS) code, or a similarly standardized category code). Inother embodiments, an area may be identified by a product category, aSKU number, etc.

In one embodiment, a variable of a same category (e.g., frequency (313)or amount (315)) is defined to be aggregated over a set of mutuallyexclusive areas. A transaction is classified in only one of the mutuallyexclusive areas. For example, in one embodiment, the spending frequencyvariables (313) are defined for a set of mutually exclusive merchants ormerchant categories. Transactions falling with the same category areaggregated.

Examples of the spending frequency variables (313) and spending amountvariables (315) defined for various merchant categories (e.g., 306) inone embodiment are provided in U.S. patent application Ser. No.12/537,566, filed Aug. 7, 2009 and entitled “Cardholder Clusters,” andin Prov. U.S. Pat. App. Ser. No. 61/182,806, filed Jun. 1, 2009 andentitled “Cardholder Clusters,” the disclosures of which applicationsare hereby incorporated herein by reference.

In one embodiment, super categories (311) are defined to group thecategories (e.g., 306) used in transaction records (301). The supercategories (311) can be mutually exclusive. For example, each merchantcategory (306) is classified under only one super merchant category butnot any other super merchant categories. Since the generation of thelist of super categories typically requires deep domain knowledge aboutthe businesses of the merchants in various categories, super categories(311) are not used in one embodiment.

In one embodiment, the aggregation (317) includes the application of thedefinitions (309) for these variables (e.g., 311, 313, and 315) to thetransaction records (301) to generate the variable values (321). Thetransaction records (301) are aggregated to generate aggregatedmeasurements (e.g., variable values (321)) that are not specific to aparticular transaction, such as frequencies of purchases made withdifferent merchants or different groups of merchants, the amounts spentwith different merchants or different groups of merchants, and thenumber of unique purchases across different merchants or differentgroups of merchants, etc. The aggregation (317) can be performed for aparticular time period and for entities at various levels.

In one embodiment, the transaction records (301) are aggregatedaccording to a buying entity. The aggregation (317) can be performed ataccount level, person level, family level, company level, neighborhoodlevel, city level, region level, etc. to analyze the spending patternsacross various areas (e.g., sellers, products or services) for therespective aggregated buying entity. For example, the transactionrecords (301) for a particular account (e.g., presented by the accountnumber (302)) can be aggregated for an account level analysis. Toaggregate the transaction records (301) in account level, thetransactions with a specific merchant or merchants in a specificcategory are counted according to the variable definitions (309) for aparticular account to generate a frequency measure (e.g., 313) for theaccount relative to the specific merchant or merchant category; and thetransaction amounts (e.g., 304) with the specific merchant or thespecific category of merchants are summed for the particular account togenerate an average spending amount for the account relative to thespecific merchant or merchant category. For example, the transactionrecords (301) for a particular person having multiple accounts can beaggregated for a person level analysis, the transaction records (301)aggregated for a particular family for a family level analysis, and thetransaction records (301) for a particular business aggregated for abusiness level analysis.

The aggregation (317) can be performed for a predetermined time period,such as for the transactions occurring in the past month, in the pastthree months, in the past twelve months, etc.

In another embodiment, the transaction records (301) are aggregatedaccording to a selling entity. The spending patterns at the sellingentity across various buyers, products or services can be analyzed. Forexample, the transaction records (301) for a particular merchant havingtransactions with multiple accounts can be aggregated for a merchantlevel analysis. For example, the transaction records (301) for aparticular merchant group can be aggregated for a merchant group levelanalysis.

In one embodiment, the aggregation (317) is formed separately fordifferent types of transactions, such as transactions made online,offline, via phone, and/or “card-present” transactions vs.“card-not-present” transactions, which can be used to identify thespending pattern differences among different types of transactions.

In one embodiment, the variable values (e.g., 323, 324, . . . , 325)associated with an entity ID (322) are considered the random samples ofthe respective variables (e.g., 311, 313, 315), sampled for the instanceof an entity represented by the entity ID (322). Statistical analyses(e.g., factor analysis (327) and cluster analysis (329)) are performedto identify the patterns and correlations in the random samples.

For example, a cluster analysis (329) can identify a set of clusters andthus cluster definitions (333) (e.g., the locations of the centroids ofthe clusters). In one embodiment, each entity ID (322) is represented asa point in a mathematical space defined by the set of variables; and thevariable values (323, 324, . . . , 325) of the entity ID (322) determinethe coordinates of the point in the space and thus the location of thepoint in the space. Various points may be concentrated in variousregions; and the cluster analysis (329) is configured to formulate thepositioning of the points to drive the clustering of the points. Inother embodiments, the cluster analysis (329) can also be performedusing the techniques of Self Organizing Maps (SOM), which can identifyand show clusters of multi-dimensional data using a representation on atwo-dimensional map.

Once the cluster definitions (333) are obtained from the clusteranalysis (329), the identity of the cluster (e.g., cluster ID (343))that contains the entity ID (322) can be used to characterize spendingbehavior of the entity represented by the entity ID (322). The entitiesin the same cluster are considered to have similar spending behaviors.

Similarities and differences among the entities, such as accounts,individuals, families, etc., as represented by the entity ID (e.g., 322)and characterized by the variable values (e.g., 323, 324, . . . , 325)can be identified via the cluster analysis (329). In one embodiment,after a number of clusters of entity IDs are identified based on thepatterns of the aggregated measurements, a set of profiles can begenerated for the clusters to represent the characteristics of theclusters. Once the clusters are identified, each of the entity IDs(e.g., corresponding to an account, individual, family) can be assignedto one cluster; and the profile for the corresponding cluster may beused to represent, at least in part, the entity (e.g., account,individual, family). Alternatively, the relationship between an entity(e.g., an account, individual, family) and one or more clusters can bedetermined (e.g., based on a measurement of closeness to each cluster).Thus, the cluster related data can be used in a transaction profile (127or 341) to provide information about the behavior of the entity (e.g.,an account, an individual, a family).

In one embodiment, more than one set of cluster definitions (333) isgenerated from cluster analyses (329). For example, cluster analyses(329) may generate different sets of cluster solutions corresponding todifferent numbers of identified clusters. A set of cluster IDs (e.g.,343) can be used to summarize (335) the spending behavior of the entityrepresented by the entity ID (322), based on the typical spendingbehavior of the respective clusters. In one example, two clustersolutions are obtained; one of the cluster solutions has 17 clusters,which classify the entities in a relatively coarse manner; and the othercluster solution has 55 clusters, which classify the entities in arelative fine manner. A cardholder can be identified by the spendingbehavior of one of the 17 clusters and one of the 55 clusters in whichthe cardholder is located. Thus, the set of cluster IDs corresponding tothe set of cluster solutions provides a hierarchical identification ofan entity among clusters of different levels of resolution. The spendingbehavior of the clusters is represented by the cluster definitions(333), such as the parameters (e.g., variable values) that define thecentroids of the clusters.

In one embodiment, the random variables (e.g., 313 and 315) as definedby the definitions (309) have certain degrees of correlation and are notindependent from each other. For example, merchants of differentmerchant categories (e.g., 306) may have overlapping business, or havecertain business relationships. For example, certain products and/orservices of certain merchants have cause and effect relationships. Forexample, certain products and/or services of certain merchants aremutually exclusive to a certain degree (e.g., a purchase from onemerchant may have a level of probability to exclude the user (101) frommaking a purchase from another merchant). Such relationships may becomplex and difficult to quantify by merely inspecting the categories.Further, such relationships may shift over time as the economy changes.

In one embodiment, a factor analysis (327) is performed to reduce theredundancy and/or correlation among the variables (e.g., 313, 315). Thefactor analysis (327) identifies the definitions (331) for factors, eachof which represents a combination of the variables (e.g., 313, 315).

In one embodiment, a factor is a linear combination of a plurality ofthe aggregated measurements (e.g., variables (313, 315)) determined forvarious areas (e.g., merchants or merchant categories, products orproduct categories). Once the relationship between the factors and theaggregated measurements is determined via factor analysis, the valuesfor the factors can be determined from the linear combinations of theaggregated measurements and be used in a transaction profile (127 or341) to provide information on the behavior of the entity represented bythe entity ID (e.g., an account, an individual, a family).

Once the factor definitions (331) are obtained from the factor analysis(327), the factor definitions (331) can be applied to the variablevalues (321) to determine factor values (344) for the aggregatedspending profile (341). Since redundancy and correlation are reduced inthe factors, the number of factors is typically much smaller than thenumber of the original variables (e.g., 313, 315). Thus, the factorvalues (344) represent the concise summary of the original variables(e.g., 313, 315).

For example, there may be thousands of variables on spending frequencyand amount for different merchant categories; and the factor analysis(327) can reduce the factor number to less than one hundred (and evenless than twenty). In one example, a twelve-factor solution is obtained,which allows the use of twelve factors to combine the thousands of theoriginal variables (313, 315); and thus, the spending behavior inthousands of merchant categories can be summarized via twelve factorvalues (344). In one embodiment, each factor is combination of at leastfour variables; and a typical variable has contributions to more thanone factor.

In one example, hundreds or thousands of transaction records (301) of acardholder are converted into hundreds or thousands of variable values(321) for various merchant categories, which are summarized (335) viathe factor definitions (331) and cluster definitions (333) into twelvefactor values (344) and one or two cluster IDs (e.g., 343). Thesummarized data can be readily interpreted by a human to ascertain thespending behavior of the cardholder. A user (101) may easily specify aspending behavior requirement formulated based on the factor values(344) and the cluster IDs (e.g., to query for a segment of customers, orto request the targeting of a segment of customers). The reduced size ofthe summarized data reduces the need for data communication bandwidthfor communicating the spending behavior of the cardholder over a networkconnection and allows simplified processing and utilization of the datarepresenting the spending behavior of the cardholder.

In one embodiment, the behavior and characteristics of the clusters arestudied to identify a description of a type of representative entitiesthat are found in each of the clusters. The clusters can be named basedon the type of representative entities to allow an ordinary person toeasily understand the typical behavior of the clusters.

In one embodiment, the behavior and characteristics of the factors arealso studied to identify dominant aspects of each factor. The clusterscan be named based on the dominant aspects to allow an ordinary personto easily understand the meaning of a factor value.

In FIG. 2, an aggregated spending profile (341) for an entityrepresented by an entity ID (e.g., 322) includes the cluster ID (343)and factor values (344) determined based on the cluster definitions(333) and the factor definitions (331). The aggregated spending profile(341) may further include other statistical parameters, such asdiversity index (342), channel distribution (345), category distribution(346), zip code (347), etc., as further discussed below.

In one embodiment, the diversity index (342) may include an entropyvalue and/or a Gini coefficient, to represent the diversity of thespending by the entity represented by the entity ID (322) acrossdifferent areas (e.g., different merchant categories (e.g., 306)). Whenthe diversity index (342) indicates that the diversity of the spendingdata is under a predetermined threshold level, the variable values(e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) maybe excluded from the cluster analysis (329) and/or the factor analysis(327) due to the lack of diversity. When the diversity index (342) ofthe aggregated spending profile (341) is lower than a predeterminedthreshold, the factor values (344) and the cluster ID (343) may notaccurately represent the spending behavior of the corresponding entity.

In one embodiment, the channel distribution (345) includes a set ofpercentage values that indicate the percentages of amounts spent indifferent purchase channels, such as online, via phone, in a retailstore, etc.

In one embodiment, the category distribution (346) includes a set ofpercentage values that indicate the percentages of spending amounts indifferent super categories (311). In one embodiment, thousands ofdifferent merchant categories (e.g., 306) are represented by MerchantCategory Codes (MCC), or North American Industry Classification System(NAICS) codes in transaction records (301). These merchant categories(e.g., 306) are classified or combined into less than one hundred supercategories (or less than twenty). In one example, fourteen supercategories are defined based on domain knowledge.

In one embodiment, the aggregated spending profile (341) includes theaggregated measurements (e.g., frequency, average spending amount)determined for a set of predefined, mutually exclusive merchantcategories (e.g., super categories (311)). Each of the super merchantcategories represents a type of products or services a customer maypurchase. A transaction profile (127 or 341) may include the aggregatedmeasurements for each of the set of mutually exclusive merchantcategories. The aggregated measurements determined for the predefined,mutually exclusive merchant categories can be used in transactionprofiles (127 or 341) to provide information on the behavior of arespective entity (e.g., an account, an individual, or a family).

In one embodiment, the zip code (347) in the aggregated spending profile(341) represents the dominant geographic area in which the spendingassociated with the entity ID (322) occurred. Alternatively or incombination, the aggregated spending profile (341) may include adistribution of transaction amounts over a set of zip codes that accountfor a majority of the transactions or transaction amounts (e.g., 90%).

In one embodiment, the factor analysis (327) and cluster analysis (329)are used to summarize the spending behavior across various areas, suchas different merchants characterized by merchant category (306),different products and/or services, different consumers, etc. Theaggregated spending profile (341) may include more or fewer fields thanthose illustrated in FIG. 2. For example, in one embodiment, theaggregated spending profile (341) further includes an aggregatedspending amount for a period of time (e.g., the past twelve months); inanother embodiment, the aggregated spending profile (341) does notinclude the category distribution (346); and in a further embodiment,the aggregated spending profile (341) may include a set of distancemeasures to the centroids of the clusters. The distance measures may bedefined based on the variable values (323, 324, . . . , 325), or basedon the factor values (344). The factor values of the centroids of theclusters may be estimated based on the entity ID (e.g., 322) that isclosest to the centroid in the respective cluster.

Other variables can be used in place of, or in additional to, thevariables (311, 313, 315) illustrated in FIG. 2. For example, theaggregated spending profile (341) can be generated using variablesmeasuring shopping radius/distance from the primary address of theaccount holder to the merchant site for offline purchases. When suchvariables are used, the transaction patterns can be identified based atleast in part on clustering according to shopping radius/distance andgeographic regions. Similarly, the factor definition (331) may includethe consideration of the shopping radius/distance. For example, thetransaction records (301) may be aggregated based on the ranges ofshopping radius/distance and/or geographic regions. For example, thefactor analysis can be used to determine factors that naturally combinegeographical areas based on the correlations in the spending patterns invarious geographical areas.

In one embodiment, the aggregation (317) may involve the determinationof a deviation from a trend or pattern. For example, an account makes acertain number of purchases a week at a merchant over the past 6 months.However, in the past 2 weeks the number of purchases is less than theaverage number per week. A measurement of the deviation from the trendor pattern can be used (e.g., in a transaction profile (127 or 341) as aparameter, or in variable definitions (309) for the factor analysis(327) and/or the cluster analysis) to define the behavior of an account,an individual, a family, etc.

FIG. 3 shows a method to generate an aggregated spending profileaccording to one embodiment. In FIG. 3, computation models areestablished (351) for variables (e.g., 311, 313, and 315). In oneembodiment, the variables are defined in a way to capture certainaspects of the spending statistics, such as frequency, amount, etc.

In FIG. 3, data from related accounts are combined (353). For example,when an account number change has occurred for a cardholder in the timeperiod under analysis, the transaction records (301) under the differentaccount numbers of the same cardholder are combined under one accountnumber that represents the cardholder. For example, when the analysis isperformed at a person level (or family level, business level, socialgroup level, city level, or region level), the transaction records (301)in different accounts of the person (or family, business, social group,city or region) can be combined under one entity ID (322) thatrepresents the person (or family, business, social group, city orregion).

In one embodiment, recurrent/installment transactions are combined(355). For example, multiple monthly payments may be combined andconsidered as one single purchase.

In FIG. 3, account data are selected (357) according to a set ofcriteria related to activity, consistency, diversity, etc.

For example, when a cardholder uses a credit card solely to purchasegas, the diversity of the transactions by the cardholder is low. In sucha case, the transactions in the account of the cardholder may not bestatistically meaningful to represent the spending pattern of thecardholder in various merchant categories. Thus, in one embodiment, ifthe diversity of the transactions associated with an entity ID (322) isbelow a threshold, the variable values (e.g., 323, 324, . . . , 325)corresponding to the entity ID (322) are not used in the clusteranalysis (329) and/or the factor analysis (327). The diversity can beexamined based on the diversity index (342) (e.g., entropy or Ginicoefficient), or based on counting the different merchant categories inthe transactions associated with the entity ID (322); and when the countof different merchant categories is fewer than a threshold (e.g., 5),the transactions associated with the entity ID (322) are not used in thecluster analysis (329) and/or the factor analysis (327) due to the lackof diversity.

For example, when a cardholder uses a credit card only sporadically(e.g., when running out of cash), the limited transactions by thecardholder may not be statistically meaningful in representing thespending behavior of the cardholder. Thus, in one embodiment, when thenumbers of transactions associated with an entity ID (322) is below athreshold, the variable values (e.g., 323, 324, . . . , 325)corresponding to the entity ID (322) are not used in the clusteranalysis (329) and/or the factor analysis (327).

For example, when a cardholder has only used a credit card during aportion of the time period under analysis, the transaction records (301)during the time period may not reflect the consistent behavior of thecardholder for the entire time period. Consistency can be checked invarious ways. In one example, if the total number of transactions duringthe first and last months of the time period under analysis is zero, thetransactions associated with the entity ID (322) are inconsistent in thetime period and thus are not used in the cluster analysis (329) and/orthe factor analysis (327). Other criteria can be formulated to detectinconsistency in the transactions.

In FIG. 3, the computation models (e.g., as represented by the variabledefinitions (309)) are applied (359) to the remaining account data(e.g., transaction records (301)) to obtain data samples for thevariables. The data points associated with the entities, other thanthose whose transactions fail to meet the minimum requirements foractivity, consistency, diversity, etc., are used in factor analysis(327) and cluster analysis (329).

In FIG. 3, the data samples (e.g., variable values (321)) are used toperform (361) factor analysis (327) to identify factor solutions (e.g.,factor definitions (331)). The factor solutions can be adjusted (363) toimprove similarity in factor values of different sets of transactiondata (109). For example, factor definitions (331) can be applied to thetransactions in the time period under analysis (e.g., the past twelvemonths) and be applied separately to the transactions in a prior timeperiod (e.g., the twelve months before the past twelve months) to obtaintwo sets of factor values. The factor definitions (331) can be adjustedto improve the correlation between the two set of factor values.

The data samples can also be used to perform (365) cluster analysis(329) to identify cluster solutions (e.g., cluster definitions (333)).The cluster solutions can be adjusted (367) to improve similarity incluster identifications based on different sets of transaction data(109). For example, cluster definitions (333) can be applied to thetransactions in the time period under analysis (e.g., the past twelvemonths) and be applied separately to the transactions in a prior timeperiod (e.g., the twelve months before the past twelve months) to obtaintwo sets of cluster identifications for various entities. The clusterdefinitions (333) can be adjusted to improve the correlation between thetwo set of cluster identifications.

In one embodiment, the number of clusters is determined from clusteringanalysis. For example, a set of cluster seeds can be initiallyidentified and used to run a known clustering algorithm. The sizes ofdata points in the clusters are then examined. When a cluster containsless than a predetermined number of data points, the cluster may beeliminated to rerun the clustering analysis.

In one embodiment, standardizing entropy is added to the clustersolution to obtain improved results.

In one embodiment, human understandable characteristics of the factorsand clusters are identified (369) to name the factors and clusters. Forexample, when the spending behavior of a cluster appears to be thebehavior of an internet loyalist, the cluster can be named “internetloyalist” such that if a cardholder is found to be in the “internetloyalist” cluster, the spending preferences and patterns of thecardholder can be easily perceived.

In one embodiment, the factor analysis (327) and the cluster analysis(329) are performed periodically (e.g., once a year, or six months) toupdate the factor definitions (331) and the cluster definitions (333),which may change as the economy and the society change over time.

In FIG. 3, transaction data (109) are summarized (371) using the factorsolutions and cluster solutions to generate the aggregated spendingprofile (341). The aggregated spending profile (341) can be updated morefrequently than the factor solutions and cluster solutions, when the newtransaction data (109) becomes available. For example, the aggregatedspending profile (341) may be updated quarterly or monthly.

Various tweaks and adjustments can be made for the variables (e.g., 313,315) used for the factor analysis (327) and the cluster analysis (329).For example, the transaction records (301) may be filtered, weighted orconstrained, according to different rules to improve the capabilities ofthe aggregated measurements in indicating certain aspects of thespending behavior of the customers.

For example, in one embodiment, the variables (e.g., 313, 315) arenormalized and/or standardized (e.g., using statistical average, mean,and/or variance).

For example, the variables (e.g., 313, 315) for the aggregatedmeasurements can be tuned, via filtering and weighting, to predict thefuture trend of spending behavior (e.g., for advertisement selection),to identify abnormal behavior (e.g., for fraud prevention), or toidentify a change in spending pattern (e.g., for advertisement audiencemeasurement), etc. The aggregated measurements, the factor values (344),and/or the cluster ID (343) generated from the aggregated measurementscan be used in a transaction profile (127 or 341) to define the behaviorof an account, an individual, a family, etc.

In one embodiment, the transaction data (109) are aged to provide moreweight to recent data than older data. In other embodiments, thetransaction data (109) are reverse aged. In further embodiments, thetransaction data (109) are seasonally adjusted.

In one embodiment, the variables (e.g., 313, 315) are constrained toeliminate extreme outliers. For example, the minimum values and themaximum values of the spending amounts (315) may be constrained based onvalues at certain percentiles (e.g., the value at one percentile as theminimum and the value at 99 percentile as the maximum) and/or certainpredetermined values. In one embodiment, the spending frequencyvariables (313) are constrained based on values at certain percentilesand median values. For example, the minimum value for a spendingfrequency variable (313) may be constrained at P1−k×(M−P1), where P1 isthe one percentile value, M the median value, and k a predeterminedconstant (e.g., 0.1).

In one embodiment, variable pruning is performed to reduce the number ofvariables (e.g., 313, 315) that have less impact on cluster solutionsand/or factor solutions. For example, variables with standard variationless than a predetermined threshold (e.g., 0.1) may be discarded for thepurpose of cluster analysis (329). For example, analysis of variance(ANOVA) can be performed to identify and remove variables that are nomore significant than a predetermined threshold.

The aggregated spending profile (341) can provide information onspending behavior for various application areas, such as marketing,fraud detection and prevention, creditworthiness assessment, loyaltyanalytics, targeting of offers, etc.

For example, clusters can be used to optimize offers for various groupswithin an advertisement campaign. The use of factors and clusters totarget advertisement can improve the speed of producing targetingmodels. For example, using variables based on factors and clusters (andthus eliminating the need to use a large number of convention variables)can improve predictive models and increase efficiency of targeting byreducing the number of variables examined. The variables formulatedbased on factors and/or clusters can be used with other variables tobuild predictive models based on spending behaviors.

In one embodiment, the aggregated spending profile (341) can be used tomonitor risks in transactions. Factor values are typically consistentover time for each entity. An abrupt change in some of the factor valuesmay indicate a change in financial conditions, or a fraudulent use ofthe account. Models formulated using factors and clusters can be used toidentify a series of transactions that do not follow a normal patternspecified by the factor values (344) and/or the cluster ID (343).Potential bankruptcies can be predicted by analyzing the change offactor values over time; and significant changes in spending behaviormay be detected to stop and/or prevent fraudulent activities.

For example, the factor values (344) can be used in regression modelsand/or neural network models for the detection of certain behaviors orpatterns. Since factors are relatively non-collinear, the factors canwork well as independent variables. For example, factors and clusterscan be used as independent variables in tree models.

For example, surrogate accounts can be selected for the construction ofa quasi-control group. For example, for a given account A that is in onecluster, the account B that is closest to the account A in the samecluster can be selected as a surrogate account of the account B. Thecloseness can be determined by certain values in the aggregated spendingprofile (341), such as factor values (344), category distribution (346),etc. For example, a Euclidian distance defined based on the set ofvalues from the aggregated spending profile (341) can be used to comparethe distances between the accounts. Once identified, the surrogateaccount can be used to reduce or eliminate bias in measurements. Forexample, to determine the effect of an advertisement, the spendingpattern response of the account A that is exposed to the advertisementcan be compared to the spending pattern response of the account B thatis not exposed to the advertisement.

For example, the aggregated spending profile (341) can be used insegmentation and/or filtering analysis, such as selecting cardholdershaving similar spending behaviors identified via factors and/or clustersfor targeted advertisement campaigns, and selecting and determining agroup of merchants that could be potentially marketed towardscardholders originating in a given cluster (e.g., for bundled offers).For example, a query interface can be provided to allow the query toidentify a targeted population based on a set of criteria formulatedusing the values of clusters and factors.

For example, the aggregated spending profile (341) can be used in aspending comparison report, such as comparing a sub-population ofinterest against the overall population, determining how clusterdistributions and mean factor values differ, and building reports formerchants and/or issuers for benchmarking purposes. For example, reportscan be generated according to clusters in an automated way for themerchants. For example, the aggregated spending profile (341) can beused in geographic reports by identifying geographic areas wherecardholders shop most frequently and comparing predominant spendinglocations with cardholder residence locations.

In one embodiment, the profile generator (121) provides affinityrelationship data in the transaction profiles (127) so that thetransaction profiles (127) can be shared with business partners withoutcompromising the privacy of the users (101) and the transaction details.

For example, in one embodiment, the profile generator (121) is toidentify clusters of entities (e.g., accounts, cardholders, families,businesses, cities, regions, etc.) based on the spending patterns of theentities. The clusters represent entity segments identified based on thespending patterns of the entities reflected in the transaction data(109) or the transaction records (301).

In one embodiment, the clusters correspond to cells or regions in themathematical space that contain the respective groups of entities. Forexample, the mathematical space representing the characteristics ofusers (101) may be divided into clusters (cells or regions). Forexample, the cluster analysis (329) may identify one cluster in the cellor region that contains a cluster of entity IDs (e.g., 322) in the spacehaving a plurality of dimensions corresponding to the variables (e.g.,313 and 315). For example, a cluster can also be identified as a cell orregion in a space defined by the factors using the factor definitions(331) generated from the factor analysis (327).

In one embodiment, the parameters used in the aggregated spendingprofile (341) can be used to define a segment or a cluster of entities.For example, a value for the cluster ID (343) and a set of ranges forthe factor values (344) and/or other values can be used to define asegment.

In one embodiment, a set of clusters are standardized to represent thepredilection of entities in various groups for certain products orservices. For example, a set of standardized clusters can be formulatedfor people who have shopped, for example, at home improvement stores.The cardholders in the same cluster have similar spending behavior.

In one embodiment, the tendency or likelihood of a user (101) being in aparticular cluster (i.e. the user's affinity to the cell) can becharacterized using a value, based on past purchases. The same user(101) may have different affinity values for different clusters.

For example, a set of affinity values can be computed for an entity,based on the transaction records (301), to indicate the closeness orpredilection of the entity to the set of standardized clusters. Forexample, a cardholder who has a first value representing affinity of thecardholder to a first cluster may have a second value representingaffinity of the cardholder to a second cluster. For example, if aconsumer buys a lot of electronics, the affinity value of the consumerto the electronics cluster is high.

In one embodiment, other indicators are formulated across the merchantcommunity and cardholder behavior and provided in the profile (e.g., 127or 341) to indicate the risk of a transaction.

In one embodiment, the relationship of a pair of values from twodifferent clusters provides an indication of the likelihood that theuser (101) is in one of the two cells, if the user (101) is shown to bein the other cell. For example, if the likelihood of the user (101) topurchase each of two types of products is known, the scores can be usedto determine the likelihood of the user (101) buying one of the twotypes of products if the user (101) is known to be interested in theother type of products. In one embodiment, a map of the values for theclusters is used in a profile (e.g., 127 or 341) to characterize thespending behavior of the user (101) (or other types of entities, such asa family, company, neighborhood, city, or other types of groups definedby other aggregate parameters, such as time of day, etc.).

In one embodiment, the clusters and affinity information arestandardized to allow sharing between business partners, such astransaction processing organizations, search providers, and marketers.Purchase statistics and search statistics are generally described indifferent ways. For example, purchase statistics are based on merchants,merchant categories, SKU numbers, product descriptions, etc.; and searchstatistics are based on search terms. Once the clusters arestandardized, the clusters can be used to link purchase informationbased merchant categories (and/or SKU numbers, product descriptions)with search information based on search terms. Thus, search predilectionand purchase predilection can be mapped to each other.

In one embodiment, the purchase data and the search data (or other thirdparty data) are correlated based on mapping to the standardized clusters(cells or segments). The purchase data and the search data (or otherthird party data) can be used together to provide benefits or offers(e.g., coupons) to consumers. For example, standardized clusters can beused as a marketing tool to provide relevant benefits, includingcoupons, statement credits, or the like to consumers who are within orare associated with common clusters. For example, a data exchangeapparatus may obtain cluster data based on consumer search engine dataand actual payment transaction data to identify like groups ofindividuals who may respond favorably to particular types of benefits,such as coupons and statement credits.

Details about aggregated spending profile (341) in one embodiment areprovided in U.S. patent application Ser. No. 12/777,173, filed May 10,2010 and entitled “Systems and Methods to Summarize Transaction Data,”the disclosure of which is hereby incorporated herein by reference.

Transaction Data Based Portal

In FIG. 1, the transaction terminal (105) initiates the transaction fora user (101) (e.g., a customer) for processing by a transaction handler(103). The transaction handler (103) processes the transaction andstores transaction data (109) about the transaction, in connection withaccount data (111), such as the account profile of an account of theuser (101). The account data (111) may further include data about theuser (101), collected from issuers or merchants, and/or other sources,such as social networks, credit bureaus, merchant provided information,address information, etc. In one embodiment, a transaction may beinitiated by a server (e.g., based on a stored schedule for recurrentpayments).

Over a period of time, the transaction handler (103) accumulates thetransaction data (109) from transactions initiated at differenttransaction terminals (e.g., 105) for different users (e.g., 101). Thetransaction data (109) thus includes information on purchases made byvarious users (e.g., 101) at various times via different purchasesoptions (e.g., online purchase, offline purchase from a retail store,mail order, order via phone, etc.)

In one embodiment, the accumulated transaction data (109) and thecorresponding account data (111) are used to generate intelligenceinformation about the purchase behavior, pattern, preference, tendency,frequency, trend, amount and/or propensity of the users (e.g., 101), asindividuals or as a member of a group. The intelligence information canthen be used to generate, identify and/or select targeted advertisementsfor presentation to the user (101) on the point of interaction (107),during a transaction, after a transaction, or when other opportunitiesarise.

FIG. 4 shows a system to provide information based on transaction data(109) according to one embodiment. In FIG. 4, the transaction handler(103) is coupled between an issuer processor (145) and an acquirerprocessor (147) to facilitate authorization and settlement oftransactions between a consumer account (146) and a merchant account(148). The transaction handler (103) records the transactions in thedata warehouse (149). The portal (143) is coupled to the data warehouse(149) to provide information based on the transaction records (301),such as the transaction profiles (127) or aggregated spending profile(341). The portal (143) may be implemented as a web portal, a telephonegateway, a file/data server, etc.

In one embodiment, the portal (143) is configured to provideinformation, such as transaction profiles (127) to third parties.Further, the portal (143) may register certain users (101) for variousprograms, such as a loyalty program to provide rewards and/or offers tothe users (101).

In one embodiment, the portal (143) is to register the interest of users(101), or to obtain permissions from the users (101) to gather furtherinformation about the users (101), such as data capturing purchasedetails, online activities, etc.

In one embodiment, the user (101) may register via the issuer; and theregistration data in the consumer account (146) may propagate to thedata warehouse (149) upon approval from the user (101).

In one embodiment, the portal (143) is to register merchants and provideservices and/or information to merchants.

In one embodiment, the portal (143) is to receive information from thirdparties, such as search engines, merchants, web sites, etc. The thirdparty data can be correlated with the transaction data (109) to identifythe relationships between purchases and other events, such as searches,news announcements, conferences, meetings, etc., and improve theprediction capability and accuracy.

In FIG. 4, the consumer account (146) is under the control of the issuerprocessor (145). The consumer account (146) may be owned by anindividual, or an organization such as a business, a school, etc. Theconsumer account (146) may be a credit account, a debit account, or astored value account. The issuer may provide the consumer (e.g., user(101)) an account identification device (141) to identify the consumeraccount (146) using the account information (142). The respectiveconsumer of the account (146) can be called an account holder or acardholder, even when the consumer is not physically issued a card, orthe account identification device (141), in one embodiment. The issuerprocessor (145) is to charge the consumer account (146) to pay forpurchases.

In one embodiment, the account identification device (141) is a plasticcard having a magnetic strip storing account information (142)identifying the consumer account (146) and/or the issuer processor(145). Alternatively, the account identification device (141) is asmartcard having an integrated circuit chip storing at least the accountinformation (142). In one embodiment, the account identification device(141) includes a mobile phone having an integrated smartcard.

In one embodiment, the account information (142) is printed or embossedon the account identification device (141). The account information(142) may be printed as a bar code to allow the transaction terminal(105) to read the information via an optical scanner. The accountinformation (142) may be stored in a memory of the accountidentification device (141) and configured to be read via wireless,contactless communications, such as near field communications viamagnetic field coupling, infrared communications, or radio frequencycommunications. Alternatively, the transaction terminal (105) mayrequire contact with the account identification device (141) to read theaccount information (142) (e.g., by reading the magnetic strip of a cardwith a magnetic strip reader).

In one embodiment, the transaction terminal (105) is configured totransmit an authorization request message to the acquirer processor(147). The authorization request includes the account information (142),an amount of payment, and information about the merchant (e.g., anindication of the merchant account (148)). The acquirer processor (147)requests the transaction handler (103) to process the authorizationrequest, based on the account information (142) received in thetransaction terminal (105). The transaction handler (103) routes theauthorization request to the issuer processor (145) and may process andrespond to the authorization request when the issuer processor (145) isnot available. The issuer processor (145) determines whether toauthorize the transaction based at least in part on a balance of theconsumer account (146).

In one embodiment, the transaction handler (103), the issuer processor(145), and the acquirer processor (147) may each include a subsystem toidentify the risk in the transaction and may reject the transactionbased on the risk assessment.

In one embodiment, the account identification device (141) includessecurity features to prevent unauthorized uses of the consumer account(146), such as a logo to show the authenticity of the accountidentification device (141), encryption to protect the accountinformation (142), etc.

In one embodiment, the transaction terminal (105) is configured tointeract with the account identification device (141) to obtain theaccount information (142) that identifies the consumer account (146)and/or the issuer processor (145). The transaction terminal (105)communicates with the acquirer processor (147) that controls themerchant account (148) of a merchant. The transaction terminal (105) maycommunicate with the acquirer processor (147) via a data communicationconnection, such as a telephone connection, an Internet connection, etc.The acquirer processor (147) is to collect payments into the merchantaccount (148) on behalf of the merchant.

In one embodiment, the transaction terminal (105) is a POS terminal at atraditional, offline, “brick and mortar” retail store. In anotherembodiment, the transaction terminal (105) is an online server thatreceives account information (142) of the consumer account (146) fromthe user (101) through a web connection. In one embodiment, the user(101) may provide account information (142) through a telephone call,via verbal communications with a representative of the merchant; and therepresentative enters the account information (142) into the transactionterminal (105) to initiate the transaction.

In one embodiment, the account information (142) can be entered directlyinto the transaction terminal (105) to make payment from the consumeraccount (146), without having to physically present the accountidentification device (141). When a transaction is initiated withoutphysically presenting an account identification device (141), thetransaction is classified as a “card-not-present” (CNP) transaction.

In one embodiment, the issuer processor (145) may control more than oneconsumer account (146); the acquirer processor (147) may control morethan one merchant account (148); and the transaction handler (103) isconnected between a plurality of issuer processors (e.g., 145) and aplurality of acquirer processors (e.g., 147). An entity (e.g., bank) mayoperate both an issuer processor (145) and an acquirer processor (147).

In one embodiment, the transaction handler (103), the issuer processor(145), the acquirer processor (147), the transaction terminal (105), theportal (143), and other devices and/or services accessing the portal(143) are connected via communications networks, such as local areanetworks, cellular telecommunications networks, wireless wide areanetworks, wireless local area networks, an intranet, and Internet. Inone embodiment, dedicated communication channels are used between thetransaction handler (103) and the issuer processor (145), between thetransaction handler (103) and the acquirer processor (147), and/orbetween the portal (143) and the transaction handler (103).

In one embodiment, the transaction handler (103) uses the data warehouse(149) to store the records about the transactions, such as thetransaction records (301) or transaction data (109). In one embodiment,the transaction handler (103) includes a powerful computer, or clusterof computers functioning as a unit, controlled by instructions stored ona computer readable medium.

In one embodiment, the transaction handler (103) is configured tosupport and deliver authorization services, exception file services, andclearing and settlement services. In one embodiment, the transactionhandler (103) has a subsystem to process authorization requests andanother subsystem to perform clearing and settlement services.

In one embodiment, the transaction handler (103) is configured toprocess different types of transactions, such credit card transactions,debit card transactions, prepaid card transactions, and other types ofcommercial transactions.

In one embodiment, the transaction handler (103) facilitates thecommunications between the issuer processor (145) and the acquirerprocessor (147).

In one embodiment, the transaction terminal (105) is configured tosubmit the authorized transactions to the acquirer processor (147) forsettlement. The amount for the settlement may be different from theamount specified in the authorization request. The transaction handler(103) is coupled between the issuer processor (145) and the acquirerprocessor (147) to facilitate the clearing and settling of thetransaction. Clearing includes the exchange of financial informationbetween the issuer processor (145) and the acquirer processor (147); andsettlement includes the exchange of funds.

In one embodiment, the issuer processor (145) is to provide funds tomake payments on behalf of the consumer account (146). The acquirerprocessor (147) is to receive the funds on behalf of the merchantaccount (148). The issuer processor (145) and the acquirer processor(147) communicate with the transaction handler (103) to coordinate thetransfer of funds for the transaction. In one embodiment, the funds aretransferred electronically.

In one embodiment, the transaction terminal (105) may submit atransaction directly for settlement, without having to separately submitan authorization request.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to organize the transactions in one or more consumeraccounts (145) of the user with one or more issuers. The user (101) mayorganize the transactions using information and/or categories identifiedin the transaction records (301), such as merchant category (306),transaction date (303), amount (304), etc. Examples and techniques inone embodiment are provided in U.S. patent application Ser. No.11/378,215, filed Mar. 16, 2006, assigned Pub. No. 2007/0055597, andentitled “Method and System for Manipulating Purchase Information,” thedisclosure of which is hereby incorporated herein by reference.

In one embodiment, the portal (143) provides transaction basedstatistics, such as indicators for retail spending monitoring,indicators for merchant benchmarking, industry/market segmentation,indicators of spending patterns, etc. Further examples can be found inU.S. patent application Ser. No. 12/191,796, filed Aug. 14, 2008,assigned Pub. No. 2009/0048884, and entitled “Merchant BenchmarkingTool,” U.S. patent application Ser. No. 12/940,562, filed Nov. 5, 2010,and U.S. patent application Ser. No. 12/940,664, filed Nov. 5, 2010, thedisclosures of which applications are hereby incorporated herein byreference.

Transaction Terminal

FIG. 5 illustrates a transaction terminal according to one embodiment.In FIG. 5, the transaction terminal (105) is configured to interact withan account identification device (141) to obtain account information(142) about the consumer account (146).

In one embodiment, the transaction terminal (105) includes a memory(167) coupled to the processor (151), which controls the operations of areader (163), an input device (153), an output device (165) and anetwork interface (161). The memory (167) may store instructions for theprocessor (151) and/or data, such as an identification that isassociated with the merchant account (148).

In one embodiment, the reader (163) includes a magnetic strip reader. Inanother embodiment, the reader (163) includes a contactless reader, suchas a radio frequency identification (RFID) reader, a near fieldcommunications (NFC) device configured to read data via magnetic fieldcoupling (in accordance with ISO standard 14443/NFC), a Bluetoothtransceiver, a WiFi transceiver, an infrared transceiver, a laserscanner, etc.

In one embodiment, the input device (153) includes key buttons that canbe used to enter the account information (142) directly into thetransaction terminal (105) without the physical presence of the accountidentification device (141). The input device (153) can be configured toprovide further information to initiate a transaction, such as apersonal identification number (PIN), password, zip code, etc. that maybe used to access the account identification device (141), or incombination with the account information (142) obtained from the accountidentification device (141).

In one embodiment, the output device (165) may include a display, aspeaker, and/or a printer to present information, such as the result ofan authorization request, a receipt for the transaction, anadvertisement, etc.

In one embodiment, the network interface (161) is configured tocommunicate with the acquirer processor (147) via a telephoneconnection, an Internet connection, or a dedicated data communicationchannel.

In one embodiment, the instructions stored in the memory (167) areconfigured at least to cause the transaction terminal (105) to send anauthorization request message to the acquirer processor (147) toinitiate a transaction. The transaction terminal (105) may or may notsend a separate request for the clearing and settling of thetransaction. The instructions stored in the memory (167) are alsoconfigured to cause the transaction terminal (105) to perform othertypes of functions discussed in this description.

In one embodiment, a transaction terminal (105) may have fewercomponents than those illustrated in FIG. 5. For example, in oneembodiment, the transaction terminal (105) is configured for“card-not-present” transactions; and the transaction terminal (105) doesnot have a reader (163).

In one embodiment, a transaction terminal (105) may have more componentsthan those illustrated in FIG. 5. For example, in one embodiment, thetransaction terminal (105) is an ATM machine, which includes componentsto dispense cash under certain conditions.

Account Identification Device

FIG. 6 illustrates an account identifying device according to oneembodiment. In FIG. 6, the account identification device (141) isconfigured to carry account information (142) that identifies theconsumer account (146).

In one embodiment, the account identification device (141) includes amemory (167) coupled to the processor (151), which controls theoperations of a communication device (159), an input device (153), anaudio device (157) and a display device (155). The memory (167) maystore instructions for the processor (151) and/or data, such as theaccount information (142) associated with the consumer account (146).

In one embodiment, the account information (142) includes an identifieridentifying the issuer (and thus the issuer processor (145)) among aplurality of issuers, and an identifier identifying the consumer accountamong a plurality of consumer accounts controlled by the issuerprocessor (145). The account information (142) may include an expirationdate of the account identification device (141), the name of theconsumer holding the consumer account (146), and/or an identifieridentifying the account identification device (141) among a plurality ofaccount identification devices associated with the consumer account(146).

In one embodiment, the account information (142) may further include aloyalty program account number, accumulated rewards of the consumer inthe loyalty program, an address of the consumer, a balance of theconsumer account (146), transit information (e.g., a subway or trainpass), access information (e.g., access badges), and/or consumerinformation (e.g., name, date of birth), etc.

In one embodiment, the memory includes a nonvolatile memory, such asmagnetic strip, a memory chip, a flash memory, a Read Only Memory (ROM),etc. to store the account information (142).

In one embodiment, the information stored in the memory (167) of theaccount identification device (141) may also be in the form of datatracks that are traditionally associated with credits cards. Such tracksinclude Track 1 and Track 2. Track 1 (“International Air TransportAssociation”) stores more information than Track 2, and contains thecardholder's name as well as the account number and other discretionarydata. Track 1 is sometimes used by airlines when securing reservationswith a credit card. Track 2 (“American Banking Association”) iscurrently most commonly used and is read by ATMs and credit cardcheckers. The ABA (American Banking Association) designed thespecifications of Track 1 and banks abide by it. It contains thecardholder's account number, encrypted PIN, and other discretionarydata.

In one embodiment, the communication device (159) includes asemiconductor chip to implement a transceiver for communication with thereader (163) and an antenna to provide and/or receive wireless signals.

In one embodiment, the communication device (159) is configured tocommunicate with the reader (163). The communication device (159) mayinclude a transmitter to transmit the account information (142) viawireless transmissions, such as radio frequency signals, magneticcoupling, or infrared, Bluetooth or WiFi signals, etc.

In one embodiment, the account identification device (141) is in theform of a mobile phone, personal digital assistant (PDA), etc. The inputdevice (153) can be used to provide input to the processor (151) tocontrol the operation of the account identification device (141); andthe audio device (157) and the display device (155) may present statusinformation and/or other information, such as advertisements or offers.The account identification device (141) may include further componentsthat are not shown in FIG. 6, such as a cellular communicationssubsystem.

In one embodiment, the communication device (159) may access the accountinformation (142) stored on the memory (167) without going through theprocessor (151).

In one embodiment, the account identification device (141) has fewercomponents than those illustrated in FIG. 6. For example, an accountidentification device (141) does not have the input device (153), theaudio device (157) and the display device (155) in one embodiment; andin another embodiment, an account identification device (141) does nothave components (151-159).

For example, in one embodiment, an account identification device (141)is in the form of a debit card, a credit card, a smartcard, or aconsumer device that has optional features such as magnetic strips, orsmartcards.

An example of an account identification device (141) is a magnetic stripattached to a plastic substrate in the form of a card. The magneticstrip is used as the memory (167) of the account identification device(141) to provide the account information (142). Consumer information,such as account number, expiration date, and consumer name may beprinted or embossed on the card. A semiconductor chip implementing thememory (167) and the communication device (159) may also be embedded inthe plastic card to provide account information (142) in one embodiment.In one embodiment, the account identification device (141) has thesemiconductor chip but not the magnetic strip.

In one embodiment, the account identification device (141) is integratedwith a security device, such as an access card, a radio frequencyidentification (RFID) tag, a security card, a transponder, etc.

In one embodiment, the account identification device (141) is a handheldand compact device. In one embodiment, the account identification device(141) has a size suitable to be placed in a wallet or pocket of theconsumer.

Some examples of an account identification device (141) include a creditcard, a debit card, a stored value device, a payment card, a gift card,a smartcard, a smart media card, a payroll card, a health care card, awrist band, a keychain device, a supermarket discount card, atransponder, and a machine readable medium containing accountinformation (142).

Point of Interaction

In one embodiment, the point of interaction (107) is to provide anadvertisement to the user (101), or to provide information derived fromthe transaction data (109) to the user (101).

In one embodiment, an advertisement is a marketing interaction which mayinclude an announcement and/or an offer of a benefit, such as adiscount, incentive, reward, coupon, gift, cash back, or opportunity(e.g., special ticket/admission). An advertisement may include an offerof a product or service, an announcement of a product or service, or apresentation of a brand of products or services, or a notice of events,facts, opinions, etc. The advertisements can be presented in text,graphics, audio, video, or animation, and as printed matter, webcontent, interactive media, etc. An advertisement may be presented inresponse to the presence of a financial transaction card, or in responseto a financial transaction card being used to make a financialtransaction, or in response to other user activities, such as browsing aweb page, submitting a search request, communicating online, entering awireless communication zone, etc. In one embodiment, the presentation ofadvertisements may be not a result of a user action.

In one embodiment, the point of interaction (107) can be one of variousendpoints of the transaction network, such as point of sale (POS)terminals, automated teller machines (ATMs), electronic kiosks (orcomputer kiosks or interactive kiosks), self-assist checkout terminals,vending machines, gas pumps, websites of banks (e.g., issuer banks oracquirer banks of credit cards), bank statements (e.g., credit cardstatements), websites of the transaction handler (103), websites ofmerchants, checkout websites or web pages for online purchases, etc.

In one embodiment, the point of interaction (107) may be the same as thetransaction terminal (105), such as a point of sale (POS) terminal, anautomated teller machine (ATM), a mobile phone, a computer of the userfor an online transaction, etc. In one embodiment, the point ofinteraction (107) may be co-located with the transaction terminal (105),or produced by the transaction terminal (e.g., a receipt produced by thetransaction terminal (105)). In one embodiment, the point of interaction(107) may be separate from and not co-located with the transactionterminal (105), such as a mobile phone, a personal digital assistant, apersonal computer of the user, a voice mail box of the user, an emailinbox of the user, etc.

For example, the advertisements can be presented on a portion of mediafor a transaction with the customer, which portion might otherwise beunused and thus referred to as a “white space” herein. A white space canbe on a printed matter (e.g., a receipt printed for the transaction, ora printed credit card statement), on a video display (e.g., a displaymonitor of a POS terminal for a retail transaction, an ATM for cashwithdrawal or money transfer, a personal computer of the customer foronline purchases), or on an audio channel (e.g., an interactive voiceresponse (IVR) system for a transaction over a telephonic device).

In one embodiment, the white space is part of a media channel availableto present a message from the transaction handler (103) in connectionwith the processing of a transaction of the user (101). In oneembodiment, the white space is in a media channel that is used to reportinformation about a transaction of the user (101), such as anauthorization status, a confirmation message, a verification message, auser interface to verify a password for the online use of the accountinformation (142), a monthly statement, an alert or a report, or a webpage provided by the portal (143) to access a loyalty program associatedwith the consumer account (146) or a registration program.

In other embodiments, the advertisements can also be presented via othermedia channels which may not involve a transaction processed by thetransaction handler (103). For example, the advertisements can bepresented on publications or announcements (e.g., newspapers, magazines,books, directories, radio broadcasts, television, etc., which may be inan electronic form, or in a printed or painted form). The advertisementsmay be presented on paper, on websites, on billboards, or on audioportals.

In one embodiment, the transaction handler (103) purchases the rights touse the media channels from the owner or operators of the media channelsand uses the media channels as advertisement spaces. For example, whitespaces at a point of interaction (e.g., 107) with customers fortransactions processed by the transaction handler (103) can be used todeliver advertisements relevant to the customers conducting thetransactions; and the advertisement can be selected based at least inpart on the intelligence information derived from the accumulatedtransaction data (109) and/or the context at the point of interaction(107) and/or the transaction terminal (105).

In general, a point of interaction (e.g., 107) may or may not be capableof receiving inputs from the customers, and may or may not co-locatedwith a transaction terminal (e.g., 105) that initiates the transactions.The white spaces for presenting the advertisement on the point ofinteraction (107) may be on a portion of a geographical display space(e.g., on a screen), or on a temporal space (e.g., in an audio stream).

In one embodiment, the point of interaction (107) may be used toprimarily to access services not provided by the transaction handler(103), such as services provided by a search engine, a social networkingwebsite, an online marketplace, a blog, a news site, a televisionprogram provider, a radio station, a satellite, a publisher, etc.

In one embodiment, a consumer device is used as the point of interaction(107), which may be a non-portable consumer device or a portablecomputing device. The consumer device is to provide media content to theuser (101) and may receive input from the user (101).

Examples of non-portable consumer devices include a computer terminal, atelevision set, a personal computer, a set-top box, or the like.Examples of portable consumer devices include a portable computer, acellular phone, a personal digital assistant (PDA), a pager, a securitycard, a wireless terminal, or the like. The consumer device may beimplemented as a data processing system as illustrated in FIG. 7, withmore or fewer components.

In one embodiment, the consumer device includes an accountidentification device (141). For example, a smart card used as anaccount identification device (141) is integrated with a mobile phone,or a personal digital assistant (PDA).

In one embodiment, the point of interaction (107) is integrated with atransaction terminal (105). For example, a self-service checkoutterminal includes a touch pad to interact with the user (101); and anATM machine includes a user interface subsystem to interact with theuser (101).

Hardware

In one embodiment, a computing apparatus is configured to include someof the modules or components illustrated in FIGS. 1 and 4, such as thetransaction handler (103), the profile generator (121), the mediacontroller (115), the portal (143), the profile selector (129), theadvertisement selector (133), the user tracker (113), the correlator,and their associated storage devices, such as the data warehouse (149).

In one embodiment, at least some of the modules or componentsillustrated in FIGS. 1 and 4, such as the transaction handler (103), thetransaction terminal (105), the point of interaction (107), the usertracker (113), the media controller (115), the correlator (117), theprofile generator (121), the profile selector (129), the advertisementselector (133), the portal (143), the issuer processor (145), theacquirer processor (147), and the account identification device (141),can be implemented as a computer system, such as a data processingsystem illustrated in FIG. 7, with more or fewer components. Some of themodules may share hardware or be combined on a computer system. In oneembodiment, a network of computers can be used to implement one or moreof the modules.

Further, the data illustrated in FIG. 1, such as transaction data (109),account data (111), transaction profiles (127), and advertisement data(135), can be stored in storage devices of one or more computersaccessible to the corresponding modules illustrated in FIG. 1. Forexample, the transaction data (109) can be stored in the data warehouse(149) that can be implemented as a data processing system illustrated inFIG. 7, with more or fewer components.

In one embodiment, the transaction handler (103) is a payment processingsystem, or a payment card processor, such as a card processor for creditcards, debit cards, etc.

FIG. 7 illustrates a data processing system according to one embodiment.While FIG. 7 illustrates various components of a computer system, it isnot intended to represent any particular architecture or manner ofinterconnecting the components. One embodiment may use other systemsthat have fewer or more components than those shown in FIG. 7.

In FIG. 7, the data processing system (170) includes an inter-connect(171) (e.g., bus and system core logic), which interconnects amicroprocessor(s) (173) and memory (167). The microprocessor (173) iscoupled to cache memory (179) in the example of FIG. 7.

In one embodiment, the inter-connect (171) interconnects themicroprocessor(s) (173) and the memory (167) together and alsointerconnects them to input/output (I/O) device(s) (175) via I/Ocontroller(s) (177). I/O devices (175) may include a display deviceand/or peripheral devices, such as mice, keyboards, modems, networkinterfaces, printers, scanners, video cameras and other devices known inthe art. In one embodiment, when the data processing system is a serversystem, some of the I/O devices (175), such as printers, scanners, mice,and/or keyboards, are optional.

In one embodiment, the inter-connect (171) includes one or more busesconnected to one another through various bridges, controllers and/oradapters. In one embodiment the I/O controllers (177) include a USB(Universal Serial Bus) adapter for controlling USB peripherals, and/oran IEEE-1394 bus adapter for controlling IEEE-1394 peripherals.

In one embodiment, the memory (167) includes one or more of: ROM (ReadOnly Memory), volatile RAM (Random Access Memory), and non-volatilememory, such as hard drive, flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In this description, some functions and operations are described asbeing performed by or caused by software code to simplify description.However, such expressions are also used to specify that the functionsresult from execution of the code/instructions by a processor, such as amicroprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

Other Aspects

The foregoing description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

The use of headings herein is merely provided for ease of reference, andshall not be interpreted in any way to limit this disclosure or thefollowing claims.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,and are not necessarily all referring to separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by one embodiment and notby others. Similarly, various requirements are described which may berequirements for one embodiment but not other embodiments. Unlessexcluded by explicit description and/or apparent incompatibility, anycombination of various features described in this description is alsoincluded here.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:storing, in a computing apparatus, a route dictionary containing aplurality of route words identifying a plurality of routes previouslytraversed by a user, wherein each respective route word in the routedictionary includes an ordered sequence of symbols, each of the symbolsidentifying a predetermined vertex in the plurality of routes previouslytraversed by a user; storing, in the computing apparatus, term frequencydata of the route words of the user based on frequencies with which theuser has traversed respective routes represented by the route words;communicating, by the computing apparatus with a user device, toidentify one or more vertices of a route currently being traversed bythe user based on GPS data from the user device; generating, by thecomputing apparatus, a partial route word from a starting sequence ofsymbols representing the one or more vertices respectively; while theuser is currently traversing the route, predicting, using a textprediction technique for predicting from a given partial word a completeword in a dictionary according to term frequency, a particular routeword in the route dictionary containing the partial route word;identifying, by the computing apparatus, a remaining sequence of symbolsthat follows the starting sequence of symbols in the particular routeword; predicting, by the computing apparatus, a remaining portion of theroute that is currently being traversed by the user as being identifiedby a set of vertices corresponding to the remaining sequence of symbolsin the particular route word; identifying, by the computing apparatus, amessage based on the remaining portion of the route; and transmitting,by the computing apparatus to the user device, the message before theuser completes the predicted, remaining portion of the route.
 2. Themethod of claim 1, further comprising: monitoring, by the computingapparatus, routes traversed by the user; updating, by the computingapparatus, term frequencies of route words in the dictionarycorresponding to the routes being monitored.
 3. The method of claim 1,further comprising: accessing, by the computing apparatus, context dataassociated with the user; wherein the remaining portion of the route ispredicted based further on the context data.
 4. The method of claim 1,further comprising: monitoring, by the computing apparatus, routestraversed by the user; updating, by the computing apparatus, the routedictionary to include route words corresponding to the routes beingmonitored and having been traversed by the user.
 5. A computingapparatus having at least one microprocessor and memory storinginstructions configured to instruct the at least one microprocessor toperform operations, the computing apparatus comprising: a locationinformation receiver configured to receive, from a user device, routedata identifying routes previously traversed by a user device of a userassociated with a consumer account, and one or more vertices of aportion of a route currently being traversed by the user based on GPSdata; a route dictionary builder configured to organize the route datainto a route dictionary, wherein: the route dictionary includes aplurality of route words identifying a plurality of routes previouslytraversed by the user, each respective route word in the routedictionary includes an ordered sequence of symbols, each of symbols inthe plurality of route words identifying a predetermined vertex in theplurality of routes previously traversed by a user; and the computerapparatus stores term frequency data of the route words of the userbased on frequencies with which the user has traversed respective routesrepresented by the route words; a route predictor configured to:generate a partial route word from a starting sequence of symbolsrepresenting the one or more vertices respectively of a portion of theroute currently being traversed by the user; predict, using a textprediction technique for predicting from a given partial word a completeword in a dictionary according to term frequency, a particular routeword in the route dictionary containing the partial route word, identifya remaining sequence of symbols that follows the starting sequence ofsymbols in the particular route word, and predict a remaining portion ofthe route that is currently being traversed by the user as beingidentified by a set of vertices corresponding to the remaining sequenceof symbols in the particular route word; and a route informationtransmitter configured to: identify data based on the remaining portionof the route predicted to be identified by the set of verticescorresponding to the remaining sequence of symbols in the particularroute word, and transmit, to the user device, the data before the usercompletes the predicted, remaining portion of the route.
 6. Thecomputing apparatus of claim 5, wherein the route dictionary furthercomprises context information associated with each of the prior routestraversed by the user.
 7. The computing apparatus of claim 5, whereinthe route dictionary further comprises a count associated with each ofthe prior routes, the count representing frequency of traversal of theuser along each of the routes.
 8. The computing apparatus of claim 5,wherein the computing apparatus further comprises instructions for:updating a count of at least one of the routes when the user traversesalong the at least one route; adding a new route to the route dictionarywhen it is determined that a route traversed by a user is not encoded inthe route dictionary; and initializing a count of the new route to oneupon adding the new route to the route dictionary.
 9. A non-transitorycomputer storage medium storing instructions configured to instruct acomputing apparatus to perform a method, the method comprising: storing,in a computing apparatus, a route dictionary containing a plurality ofroute words identifying a plurality of routes previously traversed by auser, wherein each respective route word in the route dictionaryincludes an ordered sequence of symbols, each of the symbols identifyinga predetermined vertex in the plurality of routes previously traversedby a user; storing, in the computing apparatus, term frequency data ofthe route words of the user based on frequencies with which the user hastraversed respective routes represented by the route words;communicating, by the computing apparatus with a user device, toidentify one or more vertices of a route currently being traversed bythe user based on GPS data from the user device; generating, by thecomputing apparatus, a partial route word from a starting sequence ofsymbols representing the one or more vertices respectively; while theuser is currently traversing the route, predicting, using a textprediction technique for predicting from a given partial word a completeword in a dictionary according to term frequency, a particular routeword in the route dictionary containing the partial route word;identifying, by the computing apparatus, a remaining sequence of symbolsthat follows the starting sequence of symbols in the particular routeword; predict, by the computing apparatus, a remaining portion of theroute that is currently being traversed by the user as being identifiedby a set of vertices corresponding to the remaining sequence of symbolsin the particular route word; identifying, by the computing apparatus, amessage based on the remaining portion of the route; and transmitting,by the computing apparatus to the user device, the message before theuser completes the predicted, remaining portion of the route.